Skip to main content

2013 | OriginalPaper | Buchkapitel

6. General Regression Models

verfasst von : Jon Wakefield

Erschienen in: Bayesian and Frequentist Regression Methods

Verlag: Springer New York

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this chapter we consider the analysis of data that are not well-modeled by the linear models described in Chap.5. We continue to assume that the responses are (conditionally) independent. We describe two model classes, generalized linear models (GLMs) and what we refer to as nonlinear models.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
1
These data correspond to individual 2 in the Theoph data, which are available in R.
 
Literatur
Zurück zum Zitat Agresti, A. (1990). Categorical data analysis. New York: Wiley.MATH Agresti, A. (1990). Categorical data analysis. New York: Wiley.MATH
Zurück zum Zitat Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B.N. Petrov & F. Csaki (Eds.), Second International Symposium on Information Theory (pp. 267–281). Budapest: Akademia Kiado. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B.N. Petrov & F. Csaki (Eds.), Second International Symposium on Information Theory (pp. 267–281). Budapest: Akademia Kiado.
Zurück zum Zitat Allen, J., Zwerdling, R., Ehrenkranz, R., Gaultier, C., Geggel, R., Greenough, A., Kleinman, R., Klijanowicz, A., Martinez, F., Ozdemir, A., Panitch, H., Nickerson, B., Stein, M., Tomezsko, J., van der Anker, J., & American Thoracic Society. (2003). Statement of the care of the child with chronic lung disease of infancy and childhood. American Journal of Respiratory and Critical Care Medicine, 168, 356–396.CrossRef Allen, J., Zwerdling, R., Ehrenkranz, R., Gaultier, C., Geggel, R., Greenough, A., Kleinman, R., Klijanowicz, A., Martinez, F., Ozdemir, A., Panitch, H., Nickerson, B., Stein, M., Tomezsko, J., van der Anker, J., & American Thoracic Society. (2003). Statement of the care of the child with chronic lung disease of infancy and childhood. American Journal of Respiratory and Critical Care Medicine168, 356–396.CrossRef
Zurück zum Zitat Altham, D. (1991). Practical statistics for medical research. Boca Raton: Chapman and Hall/CRC. Altham, D. (1991). Practical statistics for medical research. Boca Raton: Chapman and Hall/CRC.
Zurück zum Zitat Altham, P. (1969). Exact Bayesian analysis of a 2 ×2 contingency table and Fisher’s ‘exact’ significance test. Journal of the Royal Statistical Society, Series B, 31, 261–269.MathSciNet Altham, P. (1969). Exact Bayesian analysis of a 2 ×2 contingency table and Fisher’s ‘exact’ significance test. Journal of the Royal Statistical Society, Series B31, 261–269.MathSciNet
Zurück zum Zitat Arcones, M., & E. Giné. (1992). On the bootstrap of M-estimators and other statistical functionals. In R. LePage & L. Billard (Eds.), Exploring the limits of bootstrap. New York: Wiley. Arcones, M., & E. Giné. (1992). On the bootstrap of M-estimators and other statistical functionals. In R. LePage & L. Billard (Eds.), Exploring the limits of bootstrap. New York: Wiley.
Zurück zum Zitat Armitage, P., & Berry, G. (1994). Statistical methods in medical research, third edition. Oxford: Blackwell Science. Armitage, P., & Berry, G. (1994). Statistical methods in medical research, third edition. Oxford: Blackwell Science.
Zurück zum Zitat Bachrach, L., Hastie, T., Wang, M.-C., Narasimhan, B., & Marcus, R. (1999). Bone mineral acquisition in healthy Asian, Hispanic, Black and Caucasian youth. A longitudinal study. Journal of Clinical Endocrinology and Metabolism, 84, 4702–4712. Bachrach, L., Hastie, T., Wang, M.-C., Narasimhan, B., & Marcus, R. (1999). Bone mineral acquisition in healthy Asian, Hispanic, Black and Caucasian youth. A longitudinal study. Journal of Clinical Endocrinology and Metabolism84, 4702–4712.
Zurück zum Zitat Bahadur, R. (1961). A representation of the joint distribution of responses to n dichotomous items. In H. Solomon (Ed.), Studies on item analysis and prediction (pp. 158–168). Stanford: Stanford Mathematical Studies in the Social Sciences VI, Stanford University Press. Bahadur, R. (1961). A representation of the joint distribution of responses to n dichotomous items. In H. Solomon (Ed.), Studies on item analysis and prediction (pp. 158–168). Stanford: Stanford Mathematical Studies in the Social Sciences VI, Stanford University Press.
Zurück zum Zitat Barnett, V. (2009). Comparative statistical inference (3rd ed.). New York: Wiley. Barnett, V. (2009). Comparative statistical inference (3rd ed.). New York: Wiley.
Zurück zum Zitat Bartlett, M. (1957). A comment on D.V. Lindley’s statistical paradox. Biometrika, 44, 533–534. Bartlett, M. (1957). A comment on D.V. Lindley’s statistical paradox. Biometrika44, 533–534.
Zurück zum Zitat Bates, D., & Watts, D. (1980). Curvature measures of nonlinearity (with discussion). Journal of the Royal Statistical Society, Series B, 42, 1–25.MathSciNetMATH Bates, D., & Watts, D. (1980). Curvature measures of nonlinearity (with discussion). Journal of the Royal Statistical Society, Series B42, 1–25.MathSciNetMATH
Zurück zum Zitat Bates, D., & Watts, D. (1988). Nonlinear regression analysis and its applications. New York: Wiley.MATHCrossRef Bates, D., & Watts, D. (1988). Nonlinear regression analysis and its applications. New York: Wiley.MATHCrossRef
Zurück zum Zitat Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105–139.CrossRef Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning36, 105–139.CrossRef
Zurück zum Zitat Bayes, T. (1763). An essays towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418. Reprinted, with an introduction by George Barnard, in 1958 in Biometrika, 45, 293–315. Bayes, T. (1763). An essays towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London53, 370–418. Reprinted, with an introduction by George Barnard, in 1958 in Biometrika, 45, 293–315.
Zurück zum Zitat Beal, S., & Sheiner, L. (1982). Estimating population kinetics. CRC Critical Reviews in Biomedical Engineering, 8, 195–222. Beal, S., & Sheiner, L. (1982). Estimating population kinetics. CRC Critical Reviews in Biomedical Engineering8, 195–222.
Zurück zum Zitat Beale, E. (1960). Confidence regions in non-linear estimation (with discussion). Journal of the Royal Statistical Society, Series B, 22, 41–88.MathSciNetMATH Beale, E. (1960). Confidence regions in non-linear estimation (with discussion). Journal of the Royal Statistical Society, Series B22, 41–88.MathSciNetMATH
Zurück zum Zitat Beaumont, M., Wenyang, Z., & Balding, D. (2002). Approximate Bayesian computation in population genetics. Genetics, 162, 2025–2035. Beaumont, M., Wenyang, Z., & Balding, D. (2002). Approximate Bayesian computation in population genetics. Genetics162, 2025–2035.
Zurück zum Zitat Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289–300.MathSciNetMATH Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289–300.MathSciNetMATH
Zurück zum Zitat Berger, J. (2003). Could Fisher, Jeffreys and Neyman have agreed on testing? (with discussion). Statistical Science, 18, 1–32.MathSciNetMATHCrossRef Berger, J. (2003). Could Fisher, Jeffreys and Neyman have agreed on testing? (with discussion). Statistical Science18, 1–32.MathSciNetMATHCrossRef
Zurück zum Zitat Berger, J., & Bernardo, J. (1992). On the development of reference priors (with discussion). In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian statistics 4, Proceedings of the Fourth Valencia International Meeting (pp. 35–60). Oxford: Oxford University Press. Berger, J., & Bernardo, J. (1992). On the development of reference priors (with discussion). In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian statistics 4, Proceedings of the Fourth Valencia International Meeting (pp. 35–60). Oxford: Oxford University Press.
Zurück zum Zitat Berger, J. & Wolpert, R. (1988). The likelihood principle: A review, generalizations, and statistical implications. Hayward: IMS Lecture Notes. Berger, J. & Wolpert, R. (1988). The likelihood principle: A review, generalizations, and statistical implications. Hayward: IMS Lecture Notes.
Zurück zum Zitat Berk, R. (2008). Statistical learning from a regression perspective. New York: Springer.MATH Berk, R. (2008). Statistical learning from a regression perspective. New York: Springer.MATH
Zurück zum Zitat Bernardo, J. (1979). Reference posterior distributions for Bayesian inference (with discussion). Journal of the Royal Statistical Society, Series B, 41, 113–147.MathSciNetMATH Bernardo, J. (1979). Reference posterior distributions for Bayesian inference (with discussion). Journal of the Royal Statistical Society, Series B41, 113–147.MathSciNetMATH
Zurück zum Zitat Bernstein, S. (1917). Theory of probability (Russian). Moscow-Leningrad: Gostekhizdat. Bernstein, S. (1917). Theory of probability (Russian). Moscow-Leningrad: Gostekhizdat.
Zurück zum Zitat Besag, J., & Kooperberg, C. (1995). On conditional and intrinsic auto-regressions. Biometrika, 82, 733–746.MathSciNetMATH Besag, J., & Kooperberg, C. (1995). On conditional and intrinsic auto-regressions. Biometrika, 82, 733–746.MathSciNetMATH
Zurück zum Zitat Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistics and Mathematics, 43, 1–59.MATHCrossRef Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistics and Mathematics43, 1–59.MATHCrossRef
Zurück zum Zitat Bishop, Y., Feinberg, S., & Holland, P. (1975). Discrete multivariate analysis: Theory and practice. Cambridge: MIT.MATH Bishop, Y., Feinberg, S., & Holland, P. (1975). Discrete multivariate analysis: Theory and practice. Cambridge: MIT.MATH
Zurück zum Zitat Black, D. (1984). Investigation of the possible increased incidence of cancer in West Cumbria. London: Report of the Independent Advisory Group, HMSO. Black, D. (1984). Investigation of the possible increased incidence of cancer in West Cumbria. London: Report of the Independent Advisory Group, HMSO.
Zurück zum Zitat Bliss, C. (1935). The calculation of the dosage-mortality curves. Annals of Applied Biology, 22, 134–167.CrossRef Bliss, C. (1935). The calculation of the dosage-mortality curves. Annals of Applied Biology, 22, 134–167.CrossRef
Zurück zum Zitat Bowman, A., & Azzalini, A. (1997). Applied smoothing techniques for data analysis. Oxford: Oxford University Press.MATH Bowman, A., & Azzalini, A. (1997). Applied smoothing techniques for data analysis. Oxford: Oxford University Press.MATH
Zurück zum Zitat Breiman, L., & Spector, P. (1992). Submodel selection and evaluation in regression. the x-random case. International Statistical Review, 60, 291–319. Breiman, L., & Spector, P. (1992). Submodel selection and evaluation in regression. the x-random case. International Statistical Review60, 291–319.
Zurück zum Zitat Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterrey: Wadsworth.MATH Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterrey: Wadsworth.MATH
Zurück zum Zitat Breslow, N. (2005). Whither PQL? In D. Lin & P. Heagerty (Eds.), Proceedings of the Second Seattle Symposium (pp. 1–22). New York: Springer. Breslow, N. (2005). Whither PQL? In D. Lin & P. Heagerty (Eds.), Proceedings of the Second Seattle Symposium (pp. 1–22). New York: Springer.
Zurück zum Zitat Breslow, N. & Chatterjee, N. (1999). Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. Applied Statistics, 48, 457–468.MATHCrossRef Breslow, N. & Chatterjee, N. (1999). Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. Applied Statistics48, 457–468.MATHCrossRef
Zurück zum Zitat Breslow, N., & Clayton, D. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9–25.MATH Breslow, N., & Clayton, D. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association88, 9–25.MATH
Zurück zum Zitat Breslow, N., & Day, N. (1980). Statistical methods in cancer research, Volume 1- The analysis of case-control studies. Lyon: IARC Scientific Publications No. 32. Breslow, N., & Day, N. (1980). Statistical methods in cancer research, Volume 1- The analysis of case-control studies. Lyon: IARC Scientific Publications No. 32.
Zurück zum Zitat Brinkman, N. (1981). Ethanol fuel – a single-cylinder engine study of efficiency and exhaust emissions. SAE Transcations, 90, 1410–1424. Brinkman, N. (1981). Ethanol fuel – a single-cylinder engine study of efficiency and exhaust emissions. SAE Transcations90, 1410–1424.
Zurück zum Zitat Brooks, S., Gelman, A., Jones, G., & Meng, X.-L. (Eds.). (2011). Handbook of Markov chain Monte Carlo. Boca Raton: Chapman and Hall/CRC.MATH Brooks, S., Gelman, A., Jones, G., & Meng, X.-L. (Eds.). (2011). Handbook of Markov chain Monte Carlo. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Buja, A., Hastie, T., & Tibshirani, R. (1989). Linear smoothers and additive models (with discussion). Annals of Statistics, 17, 453–555.MathSciNetMATHCrossRef Buja, A., Hastie, T., & Tibshirani, R. (1989). Linear smoothers and additive models (with discussion). Annals of Statistics17, 453–555.MathSciNetMATHCrossRef
Zurück zum Zitat Buse, A. (1982). The likelihood ratio, Wald, and Lagrange multiplier tests: an expository note. The American Statistician, 36, 153–157. Buse, A. (1982). The likelihood ratio, Wald, and Lagrange multiplier tests: an expository note. The American Statistician36, 153–157.
Zurück zum Zitat Cameron, A., & Trivedi, P. (1998). Regression analysis of count data. Cambridge: Cambridge University Press.MATH Cameron, A., & Trivedi, P. (1998). Regression analysis of count data. Cambridge: Cambridge University Press.MATH
Zurück zum Zitat Carey, V., Zeger, S., & Diggle, P. (1993). Modeling multivariate binary data with alternating logistic regressions. Biometrika, 80, 517–526.MATHCrossRef Carey, V., Zeger, S., & Diggle, P. (1993). Modeling multivariate binary data with alternating logistic regressions. Biometrika80, 517–526.MATHCrossRef
Zurück zum Zitat Carlin, B., & Louis, T. (2009). Bayesian methods for data analysis (3rd ed.). Boca Raton: Chapman and Hall/CDC. Carlin, B., & Louis, T. (2009). Bayesian methods for data analysis (3rd ed.). Boca Raton: Chapman and Hall/CDC.
Zurück zum Zitat Carroll, R., & Ruppert, D. (1988). Transformations and weighting in regression. Boca Raton: Chapman and Hall/CRC. Carroll, R., & Ruppert, D. (1988). Transformations and weighting in regression. Boca Raton: Chapman and Hall/CRC.
Zurück zum Zitat Carroll, R., Ruppert, D., & Stefanski, L. (1995). Measurement error in nonlinear models. Boca Raton: Chapman and Hall/CRC.MATH Carroll, R., Ruppert, D., & Stefanski, L. (1995). Measurement error in nonlinear models. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Carroll, R., Rupert, D., Stefanski, L., & Crainiceanu, C. (2006). Measurement error in nonlinear models: A modern perspective (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATHCrossRef Carroll, R., Rupert, D., Stefanski, L., & Crainiceanu, C. (2006). Measurement error in nonlinear models: A modern perspective (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATHCrossRef
Zurück zum Zitat Casella, G., & Berger, R. (1987). Reconciling Bayesian evidence in the one-sided testing problem. Journal of the American Statistical Association, 82, 106–111.MathSciNetMATHCrossRef Casella, G., & Berger, R. (1987). Reconciling Bayesian evidence in the one-sided testing problem. Journal of the American Statistical Association82, 106–111.MathSciNetMATHCrossRef
Zurück zum Zitat Casella, G., & Berger, R. (1990). Statistical inference. Pacific Grove: Wadsworth and Brooks.MATH Casella, G., & Berger, R. (1990). Statistical inference. Pacific Grove: Wadsworth and Brooks.MATH
Zurück zum Zitat Chaloner, K., & Brant, R. (1988). A Bayesian approach to outlier detection and residual analysis. Biometrika, 75, 651–659.MathSciNetMATHCrossRef Chaloner, K., & Brant, R. (1988). A Bayesian approach to outlier detection and residual analysis. Biometrika75, 651–659.MathSciNetMATHCrossRef
Zurück zum Zitat Chan, K., & Geyer, C. (1994). Discussion of “Markov chains for exploring posterior distributions”. The Annals of Statistics, 22, 1747–1758.CrossRef Chan, K., & Geyer, C. (1994). Discussion of “Markov chains for exploring posterior distributions”. The Annals of Statistics22, 1747–1758.CrossRef
Zurück zum Zitat Chatfield, C. (1995). Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society, Series A, 158, 419–466.CrossRef Chatfield, C. (1995). Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society, Series A158, 419–466.CrossRef
Zurück zum Zitat Chaudhuri, P., & Marron, J. (1999). SiZer for exploration of structures in curves. Journal of the American Statistical Association, 94, 807–823.MathSciNetMATHCrossRef Chaudhuri, P., & Marron, J. (1999). SiZer for exploration of structures in curves. Journal of the American Statistical Association94, 807–823.MathSciNetMATHCrossRef
Zurück zum Zitat Chen, S., Donoho, D., & Saunders, M. (1998). Atomic decomposition by basis pursuit. SIAM Journal of Scientific Computing, 20, 33–61.MathSciNetCrossRef Chen, S., Donoho, D., & Saunders, M. (1998). Atomic decomposition by basis pursuit. SIAM Journal of Scientific Computing20, 33–61.MathSciNetCrossRef
Zurück zum Zitat Chipman, H., George, E., & McCulloch, R. (1998). Bayesian cart model search (with discussion). Journal of the American Statistical Association, 93, 935–960.CrossRef Chipman, H., George, E., & McCulloch, R. (1998). Bayesian cart model search (with discussion). Journal of the American Statistical Association93, 935–960.CrossRef
Zurück zum Zitat Clayton, D., & Hills, M. (1993). Statistical models in epidemiology. Oxford: Oxford University Press.MATH Clayton, D., & Hills, M. (1993). Statistical models in epidemiology. Oxford: Oxford University Press.MATH
Zurück zum Zitat Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43, 671–682.CrossRef Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics43, 671–682.CrossRef
Zurück zum Zitat Cleveland, W., Grosse, E., & Shyu, W. (1991). Local regression models. In J. Chambers & T. Hastie (Eds.), Statistical models in S (pp. 309–376). Pacific Grove: Wadsworth and Brooks/Cole. Cleveland, W., Grosse, E., & Shyu, W. (1991). Local regression models. In J. Chambers & T. Hastie (Eds.), Statistical models in S (pp. 309–376). Pacific Grove: Wadsworth and Brooks/Cole.
Zurück zum Zitat Cochran, W. (1977). Sampling techniques. New York: Wiley.MATH Cochran, W. (1977). Sampling techniques. New York: Wiley.MATH
Zurück zum Zitat Cook, R., & Weisberg, S. (1982). Residuals and influence in regression. Boca Raton: Chapman and Hall/CRC.MATH Cook, R., & Weisberg, S. (1982). Residuals and influence in regression. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Cox, D. (1972). The analysis of multivariate binary data. Journal of the Royal Statistical Society, Series C, 21, 113–120.CrossRef Cox, D. (1972). The analysis of multivariate binary data. Journal of the Royal Statistical Society, Series C21, 113–120.CrossRef
Zurück zum Zitat Cox, D. (2006). Principles of statistical inference. Cambridge: Cambridge University Press.MATHCrossRef Cox, D. (2006). Principles of statistical inference. Cambridge: Cambridge University Press.MATHCrossRef
Zurück zum Zitat Cox, D., & Hinkley, D. (1974). Theoretical statistics. Boca Raton: Chapman and Hall/CRC.MATH Cox, D., & Hinkley, D. (1974). Theoretical statistics. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Cox, D., & Reid, N. (2000). The theory of the design of experiments. Boca Raton: Chapman and Hall/CRC.MATH Cox, D., & Reid, N. (2000). The theory of the design of experiments. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Cox, D., & Snell, E. (1989). The analysis of binary data (2nd ed.). Boca Raton: Chapman and Hall/CRC. Cox, D., & Snell, E. (1989). The analysis of binary data (2nd ed.). Boca Raton: Chapman and Hall/CRC.
Zurück zum Zitat Craig, P., Goldstein, M., Seheult, A., & Smith, J. (1998). Constructing partial prior specifications for models of complex physical systems. Journal of the Royal Statistical Society, Series D, 47, 37–53.CrossRef Craig, P., Goldstein, M., Seheult, A., & Smith, J. (1998). Constructing partial prior specifications for models of complex physical systems. Journal of the Royal Statistical Society, Series D, 47, 37–53.CrossRef
Zurück zum Zitat Crainiceanu, C., Ruppert, D., & Wand, M. (2005). Bayesian analysis for penalized spline regression using WinBUGS. Journal of Statistical Software, 14, 1–24. Crainiceanu, C., Ruppert, D., & Wand, M. (2005). Bayesian analysis for penalized spline regression using WinBUGS. Journal of Statistical Software14, 1–24.
Zurück zum Zitat Craven, P., & Wabha, G. (1979). Smoothing noisy data with spline functions. Numerische Mathematik, 31, 377–403.MATHCrossRef Craven, P., & Wabha, G. (1979). Smoothing noisy data with spline functions. Numerische Mathematik31, 377–403.MATHCrossRef
Zurück zum Zitat Crowder, M. (1986). On consistency and inconsistency of estimating equations. Econometric Theory, 2, 305–330.CrossRef Crowder, M. (1986). On consistency and inconsistency of estimating equations. Econometric Theory2, 305–330.CrossRef
Zurück zum Zitat Crowder, M. (1995). On the use of a working correlation matrix in using generalized linear models for repeated measures. Biometrika, 82, 407–410.MATHCrossRef Crowder, M. (1995). On the use of a working correlation matrix in using generalized linear models for repeated measures. Biometrika82, 407–410.MATHCrossRef
Zurück zum Zitat Crowder, M., & Hand, D. (1990). Analysis of repeated measures. Boca Raton: Chapman and Hall/CRC.MATH Crowder, M., & Hand, D. (1990). Analysis of repeated measures. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Crowder, M., & Hand, D. (1996). Practical longitudinal data analysis. Boca Raton: Chapman and Hall/CRC.MATH Crowder, M., & Hand, D. (1996). Practical longitudinal data analysis. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Darby, S., Hill, D., & Doll, R. (2001). Radon: a likely carcinogen at all exposures. Annals of Oncology, 12, 1341–1351.CrossRef Darby, S., Hill, D., & Doll, R. (2001). Radon: a likely carcinogen at all exposures. Annals of Oncology12, 1341–1351.CrossRef
Zurück zum Zitat Darroch, J., Lauritzen, S., & Speed, T. (1980). Markov fields and log-linear interaction models for contingency tables. The Annals of Statistics, 8, 522–539.MathSciNetMATHCrossRef Darroch, J., Lauritzen, S., & Speed, T. (1980). Markov fields and log-linear interaction models for contingency tables. The Annals of Statistics8, 522–539.MathSciNetMATHCrossRef
Zurück zum Zitat Davidian, M., & Giltinan, D. (1995). Nonlinear models for repeated measurement data. Boca Raton: Chapman and Hall/CRC. Davidian, M., & Giltinan, D. (1995). Nonlinear models for repeated measurement data. Boca Raton: Chapman and Hall/CRC.
Zurück zum Zitat Davies, O. (1967). Statistical methods in research and production (3rd ed.). London: Olive and Boyd. Davies, O. (1967). Statistical methods in research and production (3rd ed.). London: Olive and Boyd.
Zurück zum Zitat Davison, A., & Hinkley, D. (1997). Bootstrap methods and their application. Cambridge: Cambridge University Press.MATH Davison, A., & Hinkley, D. (1997). Bootstrap methods and their application. Cambridge: Cambridge University Press.MATH
Zurück zum Zitat De Finetti, B. (1974). Theory of probability, volume 1. New York: Wiley. De Finetti, B. (1974). Theory of probability, volume 1. New York: Wiley.
Zurück zum Zitat De Finetti, B. (1975). Theory of probability, volume 2. New York: Wiley. De Finetti, B. (1975). Theory of probability, volume 2. New York: Wiley.
Zurück zum Zitat Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1–38.MathSciNetMATH Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B39(1), 1–38.MathSciNetMATH
Zurück zum Zitat Denison, D., Holmes, C., Mallick, B., & Smith, A. (2002). Bayesian methods for nonlinear classification and regression. New York: Wiley.MATH Denison, D., Holmes, C., Mallick, B., & Smith, A. (2002). Bayesian methods for nonlinear classification and regression. New York: Wiley.MATH
Zurück zum Zitat Dennis, J., Jr, & Schnabel, R. (1996). Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs: Siam.MATHCrossRef Dennis, J., Jr, & Schnabel, R. (1996). Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs: Siam.MATHCrossRef
Zurück zum Zitat Devroye, L. (1986). Non-uniform random variate generation. New York: Springer.MATH Devroye, L. (1986). Non-uniform random variate generation. New York: Springer.MATH
Zurück zum Zitat Diaconis, P., & Ylvisaker, D. (1980). Quantifying prior opinion (with discussion). In J. Bernardo, M. D. Groot, D. Lindley, & A. Smith (Eds.), Bayesian statistics 2 (pp. 133–156). Amsterdam: North Holland. Diaconis, P., & Ylvisaker, D. (1980). Quantifying prior opinion (with discussion). In J. Bernardo, M. D. Groot, D. Lindley, & A. Smith (Eds.), Bayesian statistics 2 (pp. 133–156). Amsterdam: North Holland.
Zurück zum Zitat DiCiccio, T., Kass, R., Raftery, A., & Wasserman, L. (1997). Computing Bayes factors by combining simulation and asymptotic approximations. Journal of the American Statistical Association, 92, 903–915.MathSciNetMATHCrossRef DiCiccio, T., Kass, R., Raftery, A., & Wasserman, L. (1997). Computing Bayes factors by combining simulation and asymptotic approximations. Journal of the American Statistical Association92, 903–915.MathSciNetMATHCrossRef
Zurück zum Zitat Diggle, P., & Rowlingson, B. (1994). A conditional approach to point process modelling of raised incidence. Journal of the Royal Statistical Society, Series A, 157, 433–440.CrossRef Diggle, P., & Rowlingson, B. (1994). A conditional approach to point process modelling of raised incidence. Journal of the Royal Statistical Society, Series A157, 433–440.CrossRef
Zurück zum Zitat Diggle, P., Morris, S., & Wakefield, J. (2000). Point source modelling using matched case-control data. Biostatistics, 1, 89–105.MATHCrossRef Diggle, P., Morris, S., & Wakefield, J. (2000). Point source modelling using matched case-control data. Biostatistics1, 89–105.MATHCrossRef
Zurück zum Zitat Diggle, P., Heagerty, P., Liang, K.-Y., & Zeger, S. (2002). Analysis of longitudinal data (2nd ed.). Oxford: Oxford University Press. Diggle, P., Heagerty, P., Liang, K.-Y., & Zeger, S. (2002). Analysis of longitudinal data (2nd ed.). Oxford: Oxford University Press.
Zurück zum Zitat Doob, J. (1948). Le Calcul des Probabilités et ses Applications, Chapter Application of the theory of martingales (pp. 22–28). Colloques Internationales du CNRS Paris. Doob, J. (1948). Le Calcul des Probabilités et ses Applications, Chapter Application of the theory of martingales (pp. 22–28). Colloques Internationales du CNRS Paris.
Zurück zum Zitat Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in Solobev spaces. In W. Schemp & K. Zeller (Eds.), Construction theory of functions of several variables (pp. 85–100). New York: Springer.CrossRef Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in Solobev spaces. In W. Schemp & K. Zeller (Eds.), Construction theory of functions of several variables (pp. 85–100). New York: Springer.CrossRef
Zurück zum Zitat Dwyer, J., Andrews, E., Berkey, C., Valadian, I., & Reed, R. (1983). Growth in “new” vegetarian preschool children using the Jenss-Bayley curve fitting technique. American Journal of Clinical Nutrition, 37, 815–827. Dwyer, J., Andrews, E., Berkey, C., Valadian, I., & Reed, R. (1983). Growth in “new” vegetarian preschool children using the Jenss-Bayley curve fitting technique. American Journal of Clinical Nutrition37, 815–827.
Zurück zum Zitat Efron, B. (1975). The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association, 70, 892–898.MathSciNetMATHCrossRef Efron, B. (1975). The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association70, 892–898.MathSciNetMATHCrossRef
Zurück zum Zitat Efron, B. (2008). Microarrays, empirical Bayes and the two groups model (with discussion). Statistical Science, 23, 1–47.MathSciNetCrossRef Efron, B. (2008). Microarrays, empirical Bayes and the two groups model (with discussion). Statistical Science23, 1–47.MathSciNetCrossRef
Zurück zum Zitat Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. Boca Raton: Chapman and Hall/CRC.MATH Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Efroymson, M. (1960). Multiple regression analysis. In A. Ralston & H. Wilf (Eds.), Mathematical methods for digital computers (pp. 191–203). New YOrk: Wiley. Efroymson, M. (1960). Multiple regression analysis. In A. Ralston & H. Wilf (Eds.), Mathematical methods for digital computers (pp. 191–203). New YOrk: Wiley.
Zurück zum Zitat Essenberg, J. (1952). Cigarette smoke and the incidence of primary neoplasm of the lung in the albino mouse. Science, 116, 561–562.CrossRef Essenberg, J. (1952). Cigarette smoke and the incidence of primary neoplasm of the lung in the albino mouse. Science116, 561–562.CrossRef
Zurück zum Zitat Evans, M., & Swartz, T. (2000). Approximating integrals via Monte Carlo and deterministic methods. Oxford: Oxford University Press.MATH Evans, M., & Swartz, T. (2000). Approximating integrals via Monte Carlo and deterministic methods. Oxford: Oxford University Press.MATH
Zurück zum Zitat Fan, J. (1992). Design-adaptive nonparametric regression. Journal of the American Statistical Association, 87, 1273–1294.CrossRef Fan, J. (1992). Design-adaptive nonparametric regression. Journal of the American Statistical Association87, 1273–1294.CrossRef
Zurück zum Zitat Fan, J. & I. Gijbels (1996). Local polynomial modelling and its applications. Boca Raton: Chapman and Hall/CRC.MATH Fan, J. & I. Gijbels (1996). Local polynomial modelling and its applications. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Faraway, J. (2004). Linear models with R. Boca Raton: Chapman and Hall/CRC. Faraway, J. (2004). Linear models with R. Boca Raton: Chapman and Hall/CRC.
Zurück zum Zitat Fearnhead, P., & Prangle, D. (2012). Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation (with discussion). Journal of the Royal Statistical Society, Series B, 74, 419–474.MathSciNetCrossRef Fearnhead, P., & Prangle, D. (2012). Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation (with discussion). Journal of the Royal Statistical Society, Series B74, 419–474.MathSciNetCrossRef
Zurück zum Zitat Ferguson, T. (1996). A course in large sample theory. Boca Raton: Chapman and Hall/CRC.MATH Ferguson, T. (1996). A course in large sample theory. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Feynman, R. (1951). The concept of probability in quantum mechanics. In J. Neyman (Ed.), Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability (pp. 535–541). California: University of California Press. Feynman, R. (1951). The concept of probability in quantum mechanics. In J. Neyman (Ed.), Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability (pp. 535–541). California: University of California Press.
Zurück zum Zitat Fine, P., Ponnighaus, J., Maine, N., Clarkson, J., & Bliss, L. (1986). Protective efficacy of BCG against leprosy in Northern Malawi. The Lancet, 328, 499–502.CrossRef Fine, P., Ponnighaus, J., Maine, N., Clarkson, J., & Bliss, L. (1986). Protective efficacy of BCG against leprosy in Northern Malawi. The Lancet328, 499–502.CrossRef
Zurück zum Zitat Firth, D. (1993). Recent developments in quasi-likelihood methods. In Bulletin of the international Statistical Institute, 55, 341–358. Firth, D. (1993). Recent developments in quasi-likelihood methods. In Bulletin of the international Statistical Institute, 55, 341–358.
Zurück zum Zitat Fisher, R. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.MATHCrossRef Fisher, R. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A222, 309–368.MATHCrossRef
Zurück zum Zitat Fisher, R. (1925a). Statistical methods for research workers. Edinburgh: Oliver and Boyd. Fisher, R. (1925a). Statistical methods for research workers. Edinburgh: Oliver and Boyd.
Zurück zum Zitat Fisher, R. (1925b). Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society, 22, 700–725.MATHCrossRef Fisher, R. (1925b). Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society22, 700–725.MATHCrossRef
Zurück zum Zitat Fisher, R. (1935). The logic of inductive inference (with discussion). Journal of the Royal Statistical Society, Series A, 98, 39–82. Fisher, R. (1935). The logic of inductive inference (with discussion). Journal of the Royal Statistical Society, Series A98, 39–82.
Zurück zum Zitat Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188. Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.
Zurück zum Zitat Fisher, R. (1990). Statistical methods, experimental design and scientific inference. Oxford: Oxford University Press.MATH Fisher, R. (1990). Statistical methods, experimental design and scientific inference. Oxford: Oxford University Press.MATH
Zurück zum Zitat Fitzmaurice, G., & Laird, N. (1993). A likelihood-based method for analyzing longitudinal binary responses. Biometrika, 80, 141–151.MATHCrossRef Fitzmaurice, G., & Laird, N. (1993). A likelihood-based method for analyzing longitudinal binary responses. Biometrika80, 141–151.MATHCrossRef
Zurück zum Zitat Fitzmaurice, G., Laird, N., & Rotnitzky, A. (1993). Regression models for discrete longitudinal responses (with discussion). Statistical Science, 8, 248–309.MathSciNet Fitzmaurice, G., Laird, N., & Rotnitzky, A. (1993). Regression models for discrete longitudinal responses (with discussion). Statistical Science8, 248–309.MathSciNet
Zurück zum Zitat Fitzmaurice, G., Laird, N., & Ware, J. (2004). Applied longitudinal analysis. New York: Wiley.MATH Fitzmaurice, G., Laird, N., & Ware, J. (2004). Applied longitudinal analysis. New York: Wiley.MATH
Zurück zum Zitat Fong, Y., Rue, H., & Wakefield, J. (2010). Bayesian inference for generalized linear models. Biostatistics, 11, 397–412.CrossRef Fong, Y., Rue, H., & Wakefield, J. (2010). Bayesian inference for generalized linear models. Biostatistics11, 397–412.CrossRef
Zurück zum Zitat Freund, Y., & Schapire, R. (1997). Experiments with a new boosting algorithm. In Machine Learning: Proceedings for the Thirteenth International Conference, San Fransisco (pp. 148–156). Los Altos: Morgan Kaufmann. Freund, Y., & Schapire, R. (1997). Experiments with a new boosting algorithm. In Machine Learning: Proceedings for the Thirteenth International Conference, San Fransisco (pp. 148–156). Los Altos: Morgan Kaufmann.
Zurück zum Zitat Friedman, J. (1979). A tree-structured approach to nonparametric multiple regression. In T. Gasser & M. Rosenblatt (Eds.), Smoothing techniques for curve estimation (pp. 5–22). New York: Springer.CrossRef Friedman, J. (1979). A tree-structured approach to nonparametric multiple regression. In T. Gasser & M. Rosenblatt (Eds.), Smoothing techniques for curve estimation (pp. 5–22). New York: Springer.CrossRef
Zurück zum Zitat Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Annals of Statistics, 28, 337–407.MathSciNetMATHCrossRef Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Annals of Statistics28, 337–407.MathSciNetMATHCrossRef
Zurück zum Zitat Gamerman, D. and Lopes, H. F. (2006). Markov chain Monte Carlo: Stochastic simulation for Bayesian inference (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH Gamerman, D. and Lopes, H. F. (2006). Markov chain Monte Carlo: Stochastic simulation for Bayesian inference (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Gasser, T., Stroka, L., & Jennen-Steinmetz, C. (1986). Residual variance and residual pattern in nonlinear regression. Biometrika, 73, 625–633.MathSciNetMATHCrossRef Gasser, T., Stroka, L., & Jennen-Steinmetz, C. (1986). Residual variance and residual pattern in nonlinear regression. Biometrika73, 625–633.MathSciNetMATHCrossRef
Zurück zum Zitat Gelfand, A. E., Diggle, P. J., Fuentes, M., & Guttorp, P. (Eds.). (2010). Handbook of spatial statistics. Boca Raton: Chapman and Hall/CRC.MATH Gelfand, A. E., Diggle, P. J., Fuentes, M., & Guttorp, P. (Eds.). (2010). Handbook of spatial statistics. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515–534.MathSciNetCrossRef Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis1, 515–534.MathSciNetCrossRef
Zurück zum Zitat Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.
Zurück zum Zitat Gelman, A., & Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–511.CrossRef Gelman, A., & Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science7, 457–511.CrossRef
Zurück zum Zitat Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2004). Bayesian data analysis (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2004). Bayesian data analysis (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Gibaldi, M., & Perrier, D. (1982). Pharmacokinetics (2nd ed.). New York: Marcel Dekker. Gibaldi, M., & Perrier, D. (1982). Pharmacokinetics (2nd ed.). New York: Marcel Dekker.
Zurück zum Zitat Giné, E., Götze, F., & Mason, D. (1997). When is the Student t-statistic asymptotically normal? The Annals of Probability, 25, 1514–1531.MathSciNetMATHCrossRef Giné, E., Götze, F., & Mason, D. (1997). When is the Student t-statistic asymptotically normal? The Annals of Probability25, 1514–1531.MathSciNetMATHCrossRef
Zurück zum Zitat Glynn, P., & Iglehart, D. (1990). Simulation output using standardized time series. Mathematics of Operations Research, 15, 1–16.MathSciNetMATHCrossRef Glynn, P., & Iglehart, D. (1990). Simulation output using standardized time series. Mathematics of Operations Research15, 1–16.MathSciNetMATHCrossRef
Zurück zum Zitat Gneiting, T., & Raftery, A. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102, 359–378.MathSciNetMATHCrossRef Gneiting, T., & Raftery, A. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association102, 359–378.MathSciNetMATHCrossRef
Zurück zum Zitat Godambe, V., & Heyde, C. (1987). Quasi-likelihood and optimal estimation. International Statistical Review, 55, 231–244.MathSciNetMATHCrossRef Godambe, V., & Heyde, C. (1987). Quasi-likelihood and optimal estimation. International Statistical Review55, 231–244.MathSciNetMATHCrossRef
Zurück zum Zitat Godfrey, K. (1983). Compartmental models and their applications. London: Academic. Godfrey, K. (1983). Compartmental models and their applications. London: Academic.
Zurück zum Zitat Goldstein, M., & Wooff, D. (2007). Bayes linear statistics, theory and methods. New York: Wiley.MATHCrossRef Goldstein, M., & Wooff, D. (2007). Bayes linear statistics, theory and methods. New York: Wiley.MATHCrossRef
Zurück zum Zitat Golub, G., Heath, M. & Wabha, G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21, 215–223.MathSciNetMATHCrossRef Golub, G., Heath, M. & Wabha, G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics21, 215–223.MathSciNetMATHCrossRef
Zurück zum Zitat Goodman, S. (1993). p values, hypothesis tests and likelihood: Implications for epidemiology of a neglected historical debate. American Journal of Epidemiology, 137, 485–496. Goodman, S. (1993). p values, hypothesis tests and likelihood: Implications for epidemiology of a neglected historical debate. American Journal of Epidemiology137, 485–496.
Zurück zum Zitat Gordon, L., & Olshen, R. A. (1978). Asymptotically efficient solutions to the classification problems. Annals of Statistics, 6, 515–533.MathSciNetMATHCrossRef Gordon, L., & Olshen, R. A. (1978). Asymptotically efficient solutions to the classification problems. Annals of Statistics6, 515–533.MathSciNetMATHCrossRef
Zurück zum Zitat Gordon, L., & Olshen, R. A. (1984). Almost surely consistent nonparametric regression from recursive partitioning schemes. Journal of Multivariate Analysis, 15, 147–163.MathSciNetMATHCrossRef Gordon, L., & Olshen, R. A. (1984). Almost surely consistent nonparametric regression from recursive partitioning schemes. Journal of Multivariate Analysis15, 147–163.MathSciNetMATHCrossRef
Zurück zum Zitat Gourieroux, C., Montfort, A., & Trognon, A. (1984). Pseudo-maximum likelihood methods: Theory. Econometrica, 52, 681–700.MathSciNetMATHCrossRef Gourieroux, C., Montfort, A., & Trognon, A. (1984). Pseudo-maximum likelihood methods: Theory. Econometrica52, 681–700.MathSciNetMATHCrossRef
Zurück zum Zitat Green, P., & Silverman, B. (1994). Nonparametric regression and generalized linear models. Boca Raton: Chapman and Hall/CRC.MATH Green, P., & Silverman, B. (1994). Nonparametric regression and generalized linear models. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Greenland, S., Robins, J., & Pearl, J. (1999). Confounding and collapsibility in causal inference. Statistical Science, 14, 29–46.MATHCrossRef Greenland, S., Robins, J., & Pearl, J. (1999). Confounding and collapsibility in causal inference. Statistical Science14, 29–46.MATHCrossRef
Zurück zum Zitat Gu, C. (2002). Smoothing spline ANOVA models. New York: Springer.MATH Gu, C. (2002). Smoothing spline ANOVA models. New York: Springer.MATH
Zurück zum Zitat Hand, D. and Crowder, M. (1991). Practical longitudinal data analysis. Boca Raton: Chapman and Hall/CRC Press. Hand, D. and Crowder, M. (1991). Practical longitudinal data analysis. Boca Raton: Chapman and Hall/CRC Press.
Zurück zum Zitat Haldane, J. (1948). The precision of observed values of small frequencies. Biometrika, 35, 297–303.MathSciNet Haldane, J. (1948). The precision of observed values of small frequencies. Biometrika35, 297–303.MathSciNet
Zurück zum Zitat Härdle, W., Hall, P., & Marron, J. (1988). How far are automatically chosen smoothing parameters from their optimum? Journal of the American Statistical Association, 83, 86–101.MathSciNetMATH Härdle, W., Hall, P., & Marron, J. (1988). How far are automatically chosen smoothing parameters from their optimum? Journal of the American Statistical Association83, 86–101.MathSciNetMATH
Zurück zum Zitat Hastie, T., & Tibshirani, R. (1990). Generalized additive models. Boca Raton: Chapman and Hall/CRC.MATH Hastie, T., & Tibshirani, R. (1990). Generalized additive models. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, Series B, 55, 757–796.MathSciNetMATH Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, Series B55, 757–796.MathSciNetMATH
Zurück zum Zitat Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). New York: Springer.MATHCrossRef Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). New York: Springer.MATHCrossRef
Zurück zum Zitat Hastings, W. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109.MATHCrossRef Hastings, W. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika57, 97–109.MATHCrossRef
Zurück zum Zitat Haughton, D. (1988). On the choice of a model to fit data from an exponential family. The Annals of Statistics, 16, 342–355.MathSciNetMATHCrossRef Haughton, D. (1988). On the choice of a model to fit data from an exponential family. The Annals of Statistics16, 342–355.MathSciNetMATHCrossRef
Zurück zum Zitat Haughton, D. (1989). Size of the error in the choice of a model to fit from an exponential family. Sankhya: The Indian Journal of Statistics, Series A, 51, 45–58.MathSciNetMATH Haughton, D. (1989). Size of the error in the choice of a model to fit from an exponential family. Sankhya: The Indian Journal of Statistics, Series A51, 45–58.MathSciNetMATH
Zurück zum Zitat Heagerty, P., Kurland, B. (2001). Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika, 88, 973–986.MathSciNetMATHCrossRef Heagerty, P., Kurland, B. (2001). Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika88, 973–986.MathSciNetMATHCrossRef
Zurück zum Zitat Heyde, C. (1997). Quasi-likelihood and its applications. New York: Springer.CrossRef Heyde, C. (1997). Quasi-likelihood and its applications. New York: Springer.CrossRef
Zurück zum Zitat Hobert, J., & Casella, G. (1996). The effect of improper priors on Gibbs sampling in hierarchical linear mixed models. Journal of the American Statistical Association, 91, 1461–1473.MathSciNetMATHCrossRef Hobert, J., & Casella, G. (1996). The effect of improper priors on Gibbs sampling in hierarchical linear mixed models. Journal of the American Statistical Association91, 1461–1473.MathSciNetMATHCrossRef
Zurück zum Zitat Hodges, J., & Reich, B. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. The American Statistician, 64, 325–334.MathSciNetMATHCrossRef Hodges, J., & Reich, B. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. The American Statistician64, 325–334.MathSciNetMATHCrossRef
Zurück zum Zitat Hoerl, A., & Kennard, R. (1970). Ridge regression: Biased estimation for non-orthogonal problems. Technometrics, 12, 55–67.MATHCrossRef Hoerl, A., & Kennard, R. (1970). Ridge regression: Biased estimation for non-orthogonal problems. Technometrics12, 55–67.MATHCrossRef
Zurück zum Zitat Holst, U., Hössjer, O., Björklund, C., Ragnarson, P., & Edner, H. (1996). Locally weighted least squares kernel regression and statistical evaluation of LIDAR measurements. Environmetrics, 7, 401–416.CrossRef Holst, U., Hössjer, O., Björklund, C., Ragnarson, P., & Edner, H. (1996). Locally weighted least squares kernel regression and statistical evaluation of LIDAR measurements. Environmetrics, 7, 401–416.CrossRef
Zurück zum Zitat Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651–674.MathSciNetCrossRef Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics15, 651–674.MathSciNetCrossRef
Zurück zum Zitat Huber, P. (1967). The behavior of maximum likelihood estimators under non-standard conditions. In L. LeCam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (pp. 221–233). California: University of California Press. Huber, P. (1967). The behavior of maximum likelihood estimators under non-standard conditions. In L. LeCam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (pp. 221–233). California: University of California Press.
Zurück zum Zitat Inoue, L., & Parmigiani, G. (2009). Decision theory: Principles and approaches. New York: Wiley.MATH Inoue, L., & Parmigiani, G. (2009). Decision theory: Principles and approaches. New York: Wiley.MATH
Zurück zum Zitat Izenman, A. (2008). Modern multivariate statistical techniques: Regression, classification, and manifold learning. New York: Springer.MATH Izenman, A. (2008). Modern multivariate statistical techniques: Regression, classification, and manifold learning. New York: Springer.MATH
Zurück zum Zitat Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.MATH Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.MATH
Zurück zum Zitat Jenss, R., & Bayley, N. (1937). A mathematical method for studying the growth of a child. Human Biology, 9, 556–563. Jenss, R., & Bayley, N. (1937). A mathematical method for studying the growth of a child. Human Biology9, 556–563.
Zurück zum Zitat Johnson, N., Kotz, S., & Balakrishnan, N. (1994). Continuous univariate distributions, volume 1 (2nd ed.). New York: Wiley. Johnson, N., Kotz, S., & Balakrishnan, N. (1994). Continuous univariate distributions, volume 1 (2nd ed.). New York: Wiley.
Zurück zum Zitat Johnson, N., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, volume 2 (2nd ed.). New York: Wiley. Johnson, N., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, volume 2 (2nd ed.). New York: Wiley.
Zurück zum Zitat Johnson, N., Kotz, S., & Balakrishnan, N. (1997). Discrete multivariate distributions. New York: Wiley.MATH Johnson, N., Kotz, S., & Balakrishnan, N. (1997). Discrete multivariate distributions. New York: Wiley.MATH
Zurück zum Zitat Johnson, N., Kemp, A., & Kotz, S. (2005). Univariate discrete distributions (3rd ed.). New York: Wiley.MATHCrossRef Johnson, N., Kemp, A., & Kotz, S. (2005). Univariate discrete distributions (3rd ed.). New York: Wiley.MATHCrossRef
Zurück zum Zitat Johnson, V. (2008). Bayes factors based on test statistics. Journal of the Royal Statistical Society, Series B, 67, 689–701. Johnson, V. (2008). Bayes factors based on test statistics. Journal of the Royal Statistical Society, Series B67, 689–701.
Zurück zum Zitat Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L. (1999). An introduction to variational methods for graphical models. Machine Learning, 37, 183–233.MATHCrossRef Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L. (1999). An introduction to variational methods for graphical models. Machine Learning37, 183–233.MATHCrossRef
Zurück zum Zitat Kadane, J., & Wolfson, L. (1998). Experiences in elicitation. Journal of the Royal Statistical Society, Series D, 47, 3–19.CrossRef Kadane, J., & Wolfson, L. (1998). Experiences in elicitation. Journal of the Royal Statistical Society, Series D47, 3–19.CrossRef
Zurück zum Zitat Kalbfleisch, J., & Prentice, R. (2002). The statistical analysis of failure time data (2nd ed.). New York: Wiley.MATHCrossRef Kalbfleisch, J., & Prentice, R. (2002). The statistical analysis of failure time data (2nd ed.). New York: Wiley.MATHCrossRef
Zurück zum Zitat Kass, R., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.MATHCrossRef Kass, R., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association90, 773–795.MATHCrossRef
Zurück zum Zitat Kass, R., & Vaidyanathan, S. (1992). Approximate Bayes factors and orthogonal parameters, with application to testing equality of two binomial proportions. Journal of the Royal Statistical Society, Series B, 54, 129–144.MathSciNetMATH Kass, R., & Vaidyanathan, S. (1992). Approximate Bayes factors and orthogonal parameters, with application to testing equality of two binomial proportions. Journal of the Royal Statistical Society, Series B54, 129–144.MathSciNetMATH
Zurück zum Zitat Kass, R., Tierney, L., & Kadane, J. (1990). The validity of posterior expansions based on Laplace’s method. In S. Geisser, J. Hodges, S. Press, & A. Zellner (Eds.), Bayesian and likelihood methods in statistics and econometrics (pp. 473–488). Amsterdam: North-Holland. Kass, R., Tierney, L., & Kadane, J. (1990). The validity of posterior expansions based on Laplace’s method. In S. Geisser, J. Hodges, S. Press, & A. Zellner (Eds.), Bayesian and likelihood methods in statistics and econometrics (pp. 473–488). Amsterdam: North-Holland.
Zurück zum Zitat Kauermann, G. (2005). A note on smoothing parameter selection for penalized spline smoothing. Journal of Statistical Planning and Inference, 127, 53–69.MathSciNetMATHCrossRef Kauermann, G. (2005). A note on smoothing parameter selection for penalized spline smoothing. Journal of Statistical Planning and Inference127, 53–69.MathSciNetMATHCrossRef
Zurück zum Zitat Kauermann, G., & Carroll, R. (2001). A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association, 96, 1387–1396.MathSciNetMATHCrossRef Kauermann, G., & Carroll, R. (2001). A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association96, 1387–1396.MathSciNetMATHCrossRef
Zurück zum Zitat Kemp, I., Boyle, P., Smans, M., & Muir, C. (1985). Atlas of cancer in Scotland, 1975–1980: Incidence and epidemiologic perspective. Lyon: IARC Scientific Publication No. 72. Kemp, I., Boyle, P., Smans, M., & Muir, C. (1985). Atlas of cancer in Scotland, 1975–1980: Incidence and epidemiologic perspective. Lyon: IARC Scientific Publication No. 72.
Zurück zum Zitat Kerr, K. (2009). Comments on the analysis of unbalanced microarray data. Bioinformatics, 25, 2035–2041.CrossRef Kerr, K. (2009). Comments on the analysis of unbalanced microarray data. Bioinformatics25, 2035–2041.CrossRef
Zurück zum Zitat Kim, H., & Loh, W.-Y. (2001). Classification trees with unbiased multiway splits. Journal of the American Statistical Association, 96, 589–604.MathSciNetCrossRef Kim, H., & Loh, W.-Y. (2001). Classification trees with unbiased multiway splits. Journal of the American Statistical Association96, 589–604.MathSciNetCrossRef
Zurück zum Zitat Knafl, G., Sacks, J., & Ylvisaker, D. (1985). Confidence bands for regression functions. Journal of the American Statistical Association, 80, 683–691.MathSciNetMATHCrossRef Knafl, G., Sacks, J., & Ylvisaker, D. (1985). Confidence bands for regression functions. Journal of the American Statistical Association80, 683–691.MathSciNetMATHCrossRef
Zurück zum Zitat Knorr-Held, L., & Rasser, G. (2000). Bayesian detection of clusters and discontinuities in disease maps. Biometrics, 56, 13–21.MATHCrossRef Knorr-Held, L., & Rasser, G. (2000). Bayesian detection of clusters and discontinuities in disease maps. Biometrics56, 13–21.MATHCrossRef
Zurück zum Zitat Kosorok, M. (2008). Introduction to empirical processes and semiparametric inference. New York: Springer.MATHCrossRef Kosorok, M. (2008). Introduction to empirical processes and semiparametric inference. New York: Springer.MATHCrossRef
Zurück zum Zitat Kotz, S., Balakrishnan, N., & Johnson, N. (2000). Continuous multivariate distributions, volume 1 (2nd ed.). New York: Wiley.CrossRef Kotz, S., Balakrishnan, N., & Johnson, N. (2000). Continuous multivariate distributions, volume 1 (2nd ed.). New York: Wiley.CrossRef
Zurück zum Zitat Laird, N., & Ware, J. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.MATHCrossRef Laird, N., & Ware, J. (1982). Random-effects models for longitudinal data. Biometrics38, 963–974.MATHCrossRef
Zurück zum Zitat Lehmann, E. (1986). Testing statistical hypotheses (2nd ed.). New York: Wiley.MATH Lehmann, E. (1986). Testing statistical hypotheses (2nd ed.). New York: Wiley.MATH
Zurück zum Zitat van der Lende, R., Kok, T., Peset, R., Quanjer, P., Schouten, J., & Orie, N. G. (1981). Decreases in VC and FEV1 with time: Indicators for effects of smoking and air pollution. Bulletin of European Physiopathology and Respiration, 17, 775–792. van der Lende, R., Kok, T., Peset, R., Quanjer, P., Schouten, J., & Orie, N. G. (1981). Decreases in VC and FEV1 with time: Indicators for effects of smoking and air pollution. Bulletin of European Physiopathology and Respiration17, 775–792.
Zurück zum Zitat Liang, K.-Y., & McCullagh, P. (1993). Case studies in binary dispersion. Biometrics, 49, 623–630.CrossRef Liang, K.-Y., & McCullagh, P. (1993). Case studies in binary dispersion. Biometrics49, 623–630.CrossRef
Zurück zum Zitat Liang, K.-Y., Zeger, S., & Qaqish, B. (1992). Multivariate regression analyses for categorical data (with discussion). Journal of the Royal Statistical Society, Series B, 54, 3–40.MathSciNetMATH Liang, K.-Y., Zeger, S., & Qaqish, B. (1992). Multivariate regression analyses for categorical data (with discussion). Journal of the Royal Statistical Society, Series B54, 3–40.MathSciNetMATH
Zurück zum Zitat Lindley, D. (1968). The choice of variables in multiple regression (with discussion). Journal of the Royal Statistical Society, Series B, 30, 31–66.MathSciNetMATH Lindley, D. (1968). The choice of variables in multiple regression (with discussion). Journal of the Royal Statistical Society, Series B30, 31–66.MathSciNetMATH
Zurück zum Zitat Lindley, D. (1980). Approximate Bayesian methods. In J. Bernardo, M. D. Groot, D. Lindley, & A. Smith (Eds.), Bayesian statistics (pp. 223–237). Valencia: Valencia University Press. Lindley, D. (1980). Approximate Bayesian methods. In J. Bernardo, M. D. Groot, D. Lindley, & A. Smith (Eds.), Bayesian statistics (pp. 223–237). Valencia: Valencia University Press.
Zurück zum Zitat Lindley, D., & Smith, A. (1972). Bayes estimates for the linear model (with discussion). Journal of the Royal Statistical Society, Series B, 34, 1–41.MathSciNetMATH Lindley, D., & Smith, A. (1972). Bayes estimates for the linear model (with discussion). Journal of the Royal Statistical Society, Series B34, 1–41.MathSciNetMATH
Zurück zum Zitat Lindsey, J., Byrom, W., Wang, J., Jarvis, P., & Jones, B. (2000). Generalized nonlinear models for pharmacokinetic data. Biometrics, 56, 81–88.MATHCrossRef Lindsey, J., Byrom, W., Wang, J., Jarvis, P., & Jones, B. (2000). Generalized nonlinear models for pharmacokinetic data. Biometrics56, 81–88.MATHCrossRef
Zurück zum Zitat Lindstrom, M., & Bates, D. (1990). Nonlinear mixed-effects models for repeated measures data. Biometrics, 46, 673–687.MathSciNetCrossRef Lindstrom, M., & Bates, D. (1990). Nonlinear mixed-effects models for repeated measures data. Biometrics46, 673–687.MathSciNetCrossRef
Zurück zum Zitat Lipsitz, S., Laird, N., & Harrington, D. (1991). Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association. Biometrika, 78, 153–160.MathSciNetCrossRef Lipsitz, S., Laird, N., & Harrington, D. (1991). Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association. Biometrika78, 153–160.MathSciNetCrossRef
Zurück zum Zitat Little, R., & Rubin, D. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.MATH Little, R., & Rubin, D. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.MATH
Zurück zum Zitat Loader, C. (1999). Local regression and likelihood. New York: Springer.MATH Loader, C. (1999). Local regression and likelihood. New York: Springer.MATH
Zurück zum Zitat Lumley, T. (2010). Complex surveys: A guide to analysis using R. New York: Wiley. Lumley, T. (2010). Complex surveys: A guide to analysis using R. New York: Wiley.
Zurück zum Zitat Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual Reviews of Public Health, 23, 151–169.CrossRef Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual Reviews of Public Health23, 151–169.CrossRef
Zurück zum Zitat Machin, D., Farley, T., Busca, B., Campbell, M., & d’Arcangues, C. (1988). Assessing changes in vaginal bleeding patterns in contracepting women. Contraception, 38, 165–179. Machin, D., Farley, T., Busca, B., Campbell, M., & d’Arcangues, C. (1988). Assessing changes in vaginal bleeding patterns in contracepting women. Contraception38, 165–179.
Zurück zum Zitat Malahanobis, P. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2, 49–55. Malahanobis, P. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India2, 49–55.
Zurück zum Zitat Mallows, C. (1973). Some comments on C p . Technometrics, 15, 661–667.MATH Mallows, C. (1973). Some comments on C p . Technometrics15, 661–667.MATH
Zurück zum Zitat Marra, G., & Wood, S. (2012). Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics, 39, 53–74.MathSciNetMATHCrossRef Marra, G., & Wood, S. (2012). Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics39, 53–74.MathSciNetMATHCrossRef
Zurück zum Zitat van Marter, L., Leviton, A., Kuban, K., Pagano, M., & Allred, E. (1990). Maternal glucocorticoid therapy and reduced risk of bronchopulmonary dysplasia. Pediatrics, 86, 331–336. van Marter, L., Leviton, A., Kuban, K., Pagano, M., & Allred, E. (1990). Maternal glucocorticoid therapy and reduced risk of bronchopulmonary dysplasia. Pediatrics86, 331–336.
Zurück zum Zitat Matheron, G. (1971). The theory of regionalized variables and its applications. Technical report, Les Cahiers du Centre de Morphologie Mathématique de Fontainebleau, Fascicule 5, Ecole des Mines de Paris. Matheron, G. (1971). The theory of regionalized variables and its applications. Technical report, Les Cahiers du Centre de Morphologie Mathématique de Fontainebleau, Fascicule 5, Ecole des Mines de Paris.
Zurück zum Zitat McCullagh, P., & Nelder, J. (1989). Generalized linear models (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH McCullagh, P., & Nelder, J. (1989). Generalized linear models (2nd ed.). Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat McCulloch, C., & Neuhaus, J. (2011). Prediction of random effects in linear and generalized linear models under model misspecification. Biometrics, 67, 270–279.MathSciNetMATHCrossRef McCulloch, C., & Neuhaus, J. (2011). Prediction of random effects in linear and generalized linear models under model misspecification. Biometrics67, 270–279.MathSciNetMATHCrossRef
Zurück zum Zitat McDonald, B. (1993). Estimating logistic regression parameters for bivariate binary data. Journal of the Royal Statistical Society, Series B, 55, 391–397.MATH McDonald, B. (1993). Estimating logistic regression parameters for bivariate binary data. Journal of the Royal Statistical Society, Series B55, 391–397.MATH
Zurück zum Zitat Meier, L., van de Geer, S., & Bühlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society, Series B, 70, 53–71.MATHCrossRef Meier, L., van de Geer, S., & Bühlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society, Series B70, 53–71.MATHCrossRef
Zurück zum Zitat Meinshausen, N., & Yu, B. (2009). Lasso-type recovery of sparse representations for high-dimensional data. The Annals of Statistics, 37, 246–270.MathSciNetMATHCrossRef Meinshausen, N., & Yu, B. (2009). Lasso-type recovery of sparse representations for high-dimensional data. The Annals of Statistics37, 246–270.MathSciNetMATHCrossRef
Zurück zum Zitat Mendel, G. (1866). Versuche über Pflanzen-Hybriden. Verhandl d Naturfsch Ver in Bünn, 4, 3–47. Mendel, G. (1866). Versuche über Pflanzen-Hybriden. Verhandl d Naturfsch Ver in Bünn4, 3–47.
Zurück zum Zitat Mendel, G. (1901). Experiments in plant hybridization. Journal of the Royal Horticultural Society, 26, 1–32. Translation of Mendel (1866) by W. Bateson. Mendel, G. (1901). Experiments in plant hybridization. Journal of the Royal Horticultural Society26, 1–32. Translation of Mendel (1866) by W. Bateson.
Zurück zum Zitat Meng, X., & Wong, W. (1996). Simulating ratios of normalizing constants via a simple identity. Statistical Sinica, 6, 831–860.MathSciNetMATH Meng, X., & Wong, W. (1996). Simulating ratios of normalizing constants via a simple identity. Statistical Sinica6, 831–860.MathSciNetMATH
Zurück zum Zitat Metropolis, N., Rosenbluth, A., Teller, A., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1091.CrossRef Metropolis, N., Rosenbluth, A., Teller, A., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics21, 1087–1091.CrossRef
Zurück zum Zitat Miller, A. (1990). Subset selection in regression. Boca Raton: Chapman and Hall/CRC.MATH Miller, A. (1990). Subset selection in regression. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat von Mises, R. (1931). Wahrscheinlichkeitsrecheung. Leipzig: Franz Deutiche. von Mises, R. (1931). Wahrscheinlichkeitsrecheung. Leipzig: Franz Deutiche.
Zurück zum Zitat Montgomery, D., & Peck, E. (1982). Introduction to linear regression analysis. New York: Wiley.MATH Montgomery, D., & Peck, E. (1982). Introduction to linear regression analysis. New York: Wiley.MATH
Zurück zum Zitat Morgan, J., & Messenger, R. (1973). Thaid: a sequential search program for the analysis of nominal scale dependent variables. Technical report, Ann Arbor: Institute for Social Research, University of Michigan. Morgan, J., & Messenger, R. (1973). Thaid: a sequential search program for the analysis of nominal scale dependent variables. Technical report, Ann Arbor: Institute for Social Research, University of Michigan.
Zurück zum Zitat Morgan, J., & Sonquist, J. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58, 415–434.MATHCrossRef Morgan, J., & Sonquist, J. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association58, 415–434.MATHCrossRef
Zurück zum Zitat Nadaraya, E. (1964). On estimating regression. Theory of Probability and its Applications, 9, 141–142.CrossRef Nadaraya, E. (1964). On estimating regression. Theory of Probability and its Applications9, 141–142.CrossRef
Zurück zum Zitat Naylor, J., & Smith, A. (1982). Applications of a method for the efficient computation of posterior distributions. Applied Statistics, 31, 214–225.MathSciNetMATHCrossRef Naylor, J., & Smith, A. (1982). Applications of a method for the efficient computation of posterior distributions. Applied Statistics31, 214–225.MathSciNetMATHCrossRef
Zurück zum Zitat Nelder, J. (1966). Inverse polynomials, a useful group of multi-factor response functions. Biometrics, 22, 128–141.CrossRef Nelder, J. (1966). Inverse polynomials, a useful group of multi-factor response functions. Biometrics22, 128–141.CrossRef
Zurück zum Zitat Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370–384.CrossRef Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A135, 370–384.CrossRef
Zurück zum Zitat Neyman, J., & Pearson, E. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Part i. Philosophical Transactions of the Royal Society of London, Series A, 20A, 175–240. Neyman, J., & Pearson, E. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Part i. Philosophical Transactions of the Royal Society of London, Series A20A, 175–240.
Zurück zum Zitat Neyman, J., & Pearson, E. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, 231, 289–337.CrossRef Neyman, J., & Pearson, E. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A231, 289–337.CrossRef
Zurück zum Zitat Neyman, J., & Scott, E. (1948). Consistent estimates based on partially consistent observations. Econometrica, 16, 1–32.MathSciNetCrossRef Neyman, J., & Scott, E. (1948). Consistent estimates based on partially consistent observations. Econometrica16, 1–32.MathSciNetCrossRef
Zurück zum Zitat Nychka, D. (1988). Bayesian confidence intervals for smoothing splines. Journal of the American Statistical Association, 83, 1134–1143.MathSciNetCrossRef Nychka, D. (1988). Bayesian confidence intervals for smoothing splines. Journal of the American Statistical Association83, 1134–1143.MathSciNetCrossRef
Zurück zum Zitat O’Hagan, A. (1994). Kendall’s advanced theory of statistics, volume 2B: Bayesian inference. London: Arnold. O’Hagan, A. (1994). Kendall’s advanced theory of statistics, volume 2B: Bayesian inference. London: Arnold.
Zurück zum Zitat O’Hagan, A. (1998). Eliciting expert beliefs in substantial practical applications. Journal of the Royal Statistical Society, Series D, 47, 21–35.CrossRef O’Hagan, A. (1998). Eliciting expert beliefs in substantial practical applications. Journal of the Royal Statistical Society, Series D47, 21–35.CrossRef
Zurück zum Zitat O’Hagan, A., & Forster, J. (2004). Kendall’s advanced theory of statistics, volume 2B: Bayesian inference (2nd ed.). London: Arnold.MATH O’Hagan, A., & Forster, J. (2004). Kendall’s advanced theory of statistics, volume 2B: Bayesian inference (2nd ed.). London: Arnold.MATH
Zurück zum Zitat Pagano, M., & Gauvreau, K. (1993). Principles of biostatistics. Belmont: Duxbury Press. Pagano, M., & Gauvreau, K. (1993). Principles of biostatistics. Belmont: Duxbury Press.
Zurück zum Zitat Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). Cambridge: Cambridge University Press.MATH Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). Cambridge: Cambridge University Press.MATH
Zurück zum Zitat Pearson, E. (1953). Discussion of “Statistical inference” by D.V. Lindley. Journal of the Royal Statistical Society, Series B, 15, 68–69. Pearson, E. (1953). Discussion of “Statistical inference” by D.V. Lindley. Journal of the Royal Statistical Society, Series B15, 68–69.
Zurück zum Zitat Peers, H. (1971). Likelihood ratio and associated test criteria. Biometrika, 58, 577–587.MATHCrossRef Peers, H. (1971). Likelihood ratio and associated test criteria. Biometrika58, 577–587.MATHCrossRef
Zurück zum Zitat Pepe, M. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press.MATH Pepe, M. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press.MATH
Zurück zum Zitat Pérez, J. M., & Berger, J. O. (2002). Expected-posterior prior distributions for model selection. Biometrika, 89, 491–512.MathSciNetMATHCrossRef Pérez, J. M., & Berger, J. O. (2002). Expected-posterior prior distributions for model selection. Biometrika89, 491–512.MathSciNetMATHCrossRef
Zurück zum Zitat Pinheiro, J., & Bates, D. (2000). Mixed-effects models in S and splus. New York: Springer.CrossRef Pinheiro, J., & Bates, D. (2000). Mixed-effects models in S and splus. New York: Springer.CrossRef
Zurück zum Zitat Plummer, M. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics, 9, 523–539.MATHCrossRef Plummer, M. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics9, 523–539.MATHCrossRef
Zurück zum Zitat Potthoff, R., & Roy, S. (1964). A generalized multivariate analysis of variance useful especially for growth curve problems. Biometrika, 51, 313–326.MathSciNetMATH Potthoff, R., & Roy, S. (1964). A generalized multivariate analysis of variance useful especially for growth curve problems. Biometrika51, 313–326.MathSciNetMATH
Zurück zum Zitat Prentice, R. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44, 1033–1048.MathSciNetMATHCrossRef Prentice, R. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics44, 1033–1048.MathSciNetMATHCrossRef
Zurück zum Zitat Prentice, R., & Zhao, L. (1991). Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses. Biometrics, 47, 825–839.MathSciNetMATHCrossRef Prentice, R., & Zhao, L. (1991). Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses. Biometrics47, 825–839.MathSciNetMATHCrossRef
Zurück zum Zitat Radelet, M. (1981). Racial characteristics and the imposition of the death sentence. American Sociological Review, 46, 918–927.CrossRef Radelet, M. (1981). Racial characteristics and the imposition of the death sentence. American Sociological Review46, 918–927.CrossRef
Zurück zum Zitat Rao, C. (1948). Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proceedings of the Cambridge Philosophical Society, 44, 50–57.MATHCrossRef Rao, C. (1948). Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proceedings of the Cambridge Philosophical Society44, 50–57.MATHCrossRef
Zurück zum Zitat Rao, C., & Wu, Y. (1989). A strongly consistent procedure for model selection in a regression problem. Biometrika, 76, 369–374.MathSciNetMATHCrossRef Rao, C., & Wu, Y. (1989). A strongly consistent procedure for model selection in a regression problem. Biometrika76, 369–374.MathSciNetMATHCrossRef
Zurück zum Zitat Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge: MIT.MATH Rasmussen, C., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge: MIT.MATH
Zurück zum Zitat Ravishanker, N., & Dey, D. (2002). A first course in linear model theory. Boca Raton: Chapman and Hall/CRC.MATH Ravishanker, N., & Dey, D. (2002). A first course in linear model theory. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Reiss, P., & Ogden, R. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society, Series B, 71, 505–523.MathSciNetMATHCrossRef Reiss, P., & Ogden, R. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society, Series B71, 505–523.MathSciNetMATHCrossRef
Zurück zum Zitat Rice, K. (2008). Equivalence between conditional and random-effects likelihoods for pair-matched case-control studies. Journal of the American Statistical Association, 103, 385–396.MathSciNetMATHCrossRef Rice, K. (2008). Equivalence between conditional and random-effects likelihoods for pair-matched case-control studies. Journal of the American Statistical Association103, 385–396.MathSciNetMATHCrossRef
Zurück zum Zitat Ripley, B. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.MATH Ripley, B. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.MATH
Zurück zum Zitat Ripley, B. (2004). Selecting amongst large classes of models. In N. Adams, M. Crowder, D. Hand, & D. Stephens (Eds.), Methods and models in statistics: In honor of Professor John Nelder, FRS (pp. 155–170). London: Imperial College Press.CrossRef Ripley, B. (2004). Selecting amongst large classes of models. In N. Adams, M. Crowder, D. Hand, & D. Stephens (Eds.), Methods and models in statistics: In honor of Professor John Nelder, FRS (pp. 155–170). London: Imperial College Press.CrossRef
Zurück zum Zitat Robert, C. (2001). The Bayesian choice (2nd ed.). New York: Springer.MATH Robert, C. (2001). The Bayesian choice (2nd ed.). New York: Springer.MATH
Zurück zum Zitat Roberts, G., & Sahu, S. (1997). Updating schemes, correlation structure, blocking and parameterization for the Gibbs sampler. Journal of the Royal Statistical Society, Series B, 59, 291–317.MathSciNetMATHCrossRef Roberts, G., & Sahu, S. (1997). Updating schemes, correlation structure, blocking and parameterization for the Gibbs sampler. Journal of the Royal Statistical Society, Series B59, 291–317.MathSciNetMATHCrossRef
Zurück zum Zitat Roberts, G., Gelman, A., & Gilks, W. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability, 7, 110–120.MathSciNetMATHCrossRef Roberts, G., Gelman, A., & Gilks, W. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability7, 110–120.MathSciNetMATHCrossRef
Zurück zum Zitat Robinson, L., & Jewell, N. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review, 59, 227–240.MATHCrossRef Robinson, L., & Jewell, N. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review59, 227–240.MATHCrossRef
Zurück zum Zitat Rosenbaum, P. (2002). Observational studies (2nd ed.). New York: Springer.MATH Rosenbaum, P. (2002). Observational studies (2nd ed.). New York: Springer.MATH
Zurück zum Zitat Rothman, K., & Greenland, S. (1998). Modern epidemiology (2nd ed.). Philadelphia: Lipincott, Williams and Wilkins. Rothman, K., & Greenland, S. (1998). Modern epidemiology (2nd ed.). Philadelphia: Lipincott, Williams and Wilkins.
Zurück zum Zitat Royall, R. (1986). Model robust confidence intervals using maximum likelihood estimators. International Statistical Review, 54, 221–226.MathSciNetMATHCrossRef Royall, R. (1986). Model robust confidence intervals using maximum likelihood estimators. International Statistical Review54, 221–226.MathSciNetMATHCrossRef
Zurück zum Zitat Royall, R. (1997). Statistical evidence – a likelihood paradigm. Boca Raton: Chapman and Hall/CRC.MATH Royall, R. (1997). Statistical evidence – a likelihood paradigm. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Rue, H., & Held, L. (2005). Gaussian Markov random fields: Theory and application. Boca Raton: Chapman and Hall/CRC.CrossRef Rue, H., & Held, L. (2005). Gaussian Markov random fields: Theory and application. Boca Raton: Chapman and Hall/CRC.CrossRef
Zurück zum Zitat Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71, 319–392.MathSciNetMATHCrossRef Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B71, 319–392.MathSciNetMATHCrossRef
Zurück zum Zitat Ruppert, D., Wand, M., & Carroll, R. (2003). Semiparametric regression. Cambridge: Cambridge University Press.MATHCrossRef Ruppert, D., Wand, M., & Carroll, R. (2003). Semiparametric regression. Cambridge: Cambridge University Press.MATHCrossRef
Zurück zum Zitat Savage, L. (1972). The foundations of statistics (2nd ed.). New York: Dover.MATH Savage, L. (1972). The foundations of statistics (2nd ed.). New York: Dover.MATH
Zurück zum Zitat Scheffé, H. (1959). The analysis of variance. New York: Wiley.MATH Scheffé, H. (1959). The analysis of variance. New York: Wiley.MATH
Zurück zum Zitat Schott, J. (1997). Matrix analysis for statistics. New York: Wiley.MATH Schott, J. (1997). Matrix analysis for statistics. New York: Wiley.MATH
Zurück zum Zitat Seaman, S., & Richardson, S. (2004). Equivalence of prospective and retrospective models in the Bayesian analysis of case-control studies. Biometrika, 91, 15–25.MathSciNetMATHCrossRef Seaman, S., & Richardson, S. (2004). Equivalence of prospective and retrospective models in the Bayesian analysis of case-control studies. Biometrika91, 15–25.MathSciNetMATHCrossRef
Zurück zum Zitat Searle, S., Casella, G., & McCulloch, C. (1992). Variance components. New York: Wiley.MATHCrossRef Searle, S., Casella, G., & McCulloch, C. (1992). Variance components. New York: Wiley.MATHCrossRef
Zurück zum Zitat Sellke, T., Bayarri, M., & Berger, J. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician, 55, 62–71.MathSciNetMATHCrossRef Sellke, T., Bayarri, M., & Berger, J. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician55, 62–71.MathSciNetMATHCrossRef
Zurück zum Zitat Sheather, S., & Jones, M. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683–690.MathSciNetMATH Sheather, S., & Jones, M. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B53, 683–690.MathSciNetMATH
Zurück zum Zitat Sidák, Z. (1967). Rectangular confidence region for the means of multivariate normal distributions. Journal of the American Statistical Association, 62, 626–633.MathSciNetMATH Sidák, Z. (1967). Rectangular confidence region for the means of multivariate normal distributions. Journal of the American Statistical Association62, 626–633.MathSciNetMATH
Zurück zum Zitat Silverman, B. (1985). Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society, Series B, 47, 1–52.MATH Silverman, B. (1985). Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society, Series B47, 1–52.MATH
Zurück zum Zitat Simonoff, J. (1997). Smoothing methods in statistics. New York: Springer. Simonoff, J. (1997). Smoothing methods in statistics. New York: Springer.
Zurück zum Zitat Simpson, E. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B, 13, 238–241.MATH Simpson, E. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B13, 238–241.MATH
Zurück zum Zitat Smith, A., & Gelfand, A. (1992). Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician, 46, 84–88.MathSciNet Smith, A., & Gelfand, A. (1992). Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician46, 84–88.MathSciNet
Zurück zum Zitat Smith, C. (1947). Some examples of discrimination. Annals of Eugenics, 13, 272–282. Smith, C. (1947). Some examples of discrimination. Annals of Eugenics13, 272–282.
Zurück zum Zitat Smyth, G., & Verbyla, A. (1996). A conditional likelihood approach to residual maximum likelihood estimation in generalized linear models. Journal of the Royal Statistical Society, Series B, 58, 565–572.MathSciNetMATH Smyth, G., & Verbyla, A. (1996). A conditional likelihood approach to residual maximum likelihood estimation in generalized linear models. Journal of the Royal Statistical Society, Series B58, 565–572.MathSciNetMATH
Zurück zum Zitat Sommer, A. (1982). Nutritional blindness. Oxford: Oxford University Press. Sommer, A. (1982). Nutritional blindness. Oxford: Oxford University Press.
Zurück zum Zitat Spiegelhalter, D., Best, N., Carlin, B., & van der Linde, A. (1998). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64, 583–639. Spiegelhalter, D., Best, N., Carlin, B., & van der Linde, A. (1998). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B64, 583–639.
Zurück zum Zitat Stamey, T., Kabalin, J., McNeal, J., Johnstone, I., Freiha, F., Redwine, E., & Yang, N. (1989). Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate, II Radical prostatectomy treated patients. Journal of Urology, 141, 1076–1083. Stamey, T., Kabalin, J., McNeal, J., Johnstone, I., Freiha, F., Redwine, E., & Yang, N. (1989). Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate, II Radical prostatectomy treated patients. Journal of Urology141, 1076–1083.
Zurück zum Zitat Stone, M. (1977). An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society, Series B, 39, 44–47.MATH Stone, M. (1977). An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society, Series B39, 44–47.MATH
Zurück zum Zitat Storey, J. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64, 479–498.MathSciNetMATHCrossRef Storey, J. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B64, 479–498.MathSciNetMATHCrossRef
Zurück zum Zitat Storey, J. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics, 31, 2013–2035.MathSciNetMATHCrossRef Storey, J. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics31, 2013–2035.MathSciNetMATHCrossRef
Zurück zum Zitat Storey, J., Madeoy, J., Strout, J., Wurfel, M., Ronald, J., & Akey, J. (2007). Gene-expression variation within and among human populations. American Journal of Human Genetics, 80, 502–509.CrossRef Storey, J., Madeoy, J., Strout, J., Wurfel, M., Ronald, J., & Akey, J. (2007). Gene-expression variation within and among human populations. American Journal of Human Genetics80, 502–509.CrossRef
Zurück zum Zitat Sun, J., & Loader, C. (1994). Confidence bands for linear regression and smoothing. The Annals of Statistics, 22, 1328–1345.MathSciNetMATHCrossRef Sun, J., & Loader, C. (1994). Confidence bands for linear regression and smoothing. The Annals of Statistics22, 1328–1345.MathSciNetMATHCrossRef
Zurück zum Zitat Szpiro, A., Rice, K., & Lumley, T. (2010). Model-robust regression and a Bayesian “sandwich” estimator. Annals of Applied Statistics, 4, 2099–2113.MathSciNetMATHCrossRef Szpiro, A., Rice, K., & Lumley, T. (2010). Model-robust regression and a Bayesian “sandwich” estimator. Annals of Applied Statistics4, 2099–2113.MathSciNetMATHCrossRef
Zurück zum Zitat Thall, P., & Vail, S. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics, 46, 657–671.MathSciNetMATHCrossRef Thall, P., & Vail, S. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics46, 657–671.MathSciNetMATHCrossRef
Zurück zum Zitat Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288.MathSciNetMATH Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B58, 267–288.MathSciNetMATH
Zurück zum Zitat Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: a retrospective (with discussion). Journal of the Royal Statistical Society, Series B, 73, 273–282.MathSciNetCrossRef Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: a retrospective (with discussion). Journal of the Royal Statistical Society, Series B73, 273–282.MathSciNetCrossRef
Zurück zum Zitat Tierney, L., & Kadane, J. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81, 82–86.MathSciNetMATHCrossRef Tierney, L., & Kadane, J. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association81, 82–86.MathSciNetMATHCrossRef
Zurück zum Zitat Titterington, D., Murray, G., Murray, L., Spiegelhalter, D., Skene, A., Habbema, J., & Gelpke, G. (1981). Comparison of discrimination techniques applied to a complex data set of head injured patients. Journal of the Royal Statistical Society, Series A, 144, 145–175.MathSciNetMATHCrossRef Titterington, D., Murray, G., Murray, L., Spiegelhalter, D., Skene, A., Habbema, J., & Gelpke, G. (1981). Comparison of discrimination techniques applied to a complex data set of head injured patients. Journal of the Royal Statistical Society, Series A144, 145–175.MathSciNetMATHCrossRef
Zurück zum Zitat Upton, R., Thiercelin, J., Guentert, T., Wallace, S., Powell, J., Sansom, L., & Riegelman, S. (1982). Intraindividual variability in Theophylline pharmacokinetics: statistical verification in 39 of 60 healthy young adults. Journal of Pharmacokinetics and Biopharmaceutics, 10, 123–134. Upton, R., Thiercelin, J., Guentert, T., Wallace, S., Powell, J., Sansom, L., & Riegelman, S. (1982). Intraindividual variability in Theophylline pharmacokinetics: statistical verification in 39 of 60 healthy young adults. Journal of Pharmacokinetics and Biopharmaceutics10, 123–134.
Zurück zum Zitat van der Vaart, A. (1998). Asymptotic statistics. Cambridge: Cambridge University Press.MATH van der Vaart, A. (1998). Asymptotic statistics. Cambridge: Cambridge University Press.MATH
Zurück zum Zitat Vapnick, V. (1996). The nature of statistical learning theory. New York: Springer. Vapnick, V. (1996). The nature of statistical learning theory. New York: Springer.
Zurück zum Zitat Verbeeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer. Verbeeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer.
Zurück zum Zitat Wabha, G. (1983). Bayesian ‘confidence intervals’ for the cross-validated smoothing spline. Journal of the Royal Statistical Society, Series B, 45, 133–150. Wabha, G. (1983). Bayesian ‘confidence intervals’ for the cross-validated smoothing spline. Journal of the Royal Statistical Society, Series B45, 133–150.
Zurück zum Zitat Wabha, G. (1985). A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline problem. Annals of Statistics, 13, 1378–1402.MathSciNetCrossRef Wabha, G. (1985). A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline problem. Annals of Statistics13, 1378–1402.MathSciNetCrossRef
Zurück zum Zitat Wabha, G. (1990). Spline models for observational data. Philadelphia: SIAM. Wabha, G. (1990). Spline models for observational data. Philadelphia: SIAM.
Zurück zum Zitat Wakefield, J. (1996). Bayesian individualization via sampling-based methods. Journal of Pharmacokinetics and Biopharmaceutics, 24, 103–131. Wakefield, J. (1996). Bayesian individualization via sampling-based methods. Journal of Pharmacokinetics and Biopharmaceutics24, 103–131.
Zurück zum Zitat Wakefield, J. (2004). Non-linear regression modelling. In N. Adams, M. Crowder, D. Hand, & D. Stephens (Eds.), Methods and models in statistics: In honor of Professor John Nelder, FRS (pp. 119–153). London: Imperial College Press.CrossRef Wakefield, J. (2004). Non-linear regression modelling. In N. Adams, M. Crowder, D. Hand, & D. Stephens (Eds.), Methods and models in statistics: In honor of Professor John Nelder, FRS (pp. 119–153). London: Imperial College Press.CrossRef
Zurück zum Zitat Wakefield, J. (2007a). A Bayesian measure of the probability of false discovery in genetic epidemiology studies. American Journal of Human Genetics, 81, 208–227.CrossRef Wakefield, J. (2007a). A Bayesian measure of the probability of false discovery in genetic epidemiology studies. American Journal of Human Genetics81, 208–227.CrossRef
Zurück zum Zitat Wakefield, J. (2007b). Disease mapping and spatial regression with count data. Biostatistics, 8, 158–183.MATHCrossRef Wakefield, J. (2007b). Disease mapping and spatial regression with count data. Biostatistics, 8, 158–183.MATHCrossRef
Zurück zum Zitat Wakefield, J. (2008). Ecologic studies revisited. Annual Review of Public Health, 29, 75–90.CrossRef Wakefield, J. (2008). Ecologic studies revisited. Annual Review of Public Health29, 75–90.CrossRef
Zurück zum Zitat Wakefield, J. (2009a). Bayes factors for genome-wide association studies: Comparison with p-values. Genetic Epidemiology, 33, 79–86.CrossRef Wakefield, J. (2009a). Bayes factors for genome-wide association studies: Comparison with p-values. Genetic Epidemiology33, 79–86.CrossRef
Zurück zum Zitat Wakefield, J. (2009b). Multi-level modelling, the ecologic fallacy, and hybrid study designs. International Journal of Epidemiology, 38, 330–336.CrossRef Wakefield, J. (2009b). Multi-level modelling, the ecologic fallacy, and hybrid study designs. International Journal of Epidemiology38, 330–336.CrossRef
Zurück zum Zitat Wakefield, J., Smith, A., Racine-Poon, A., & Gelfand, A. (1994). Bayesian analysis of linear and non-linear population models using the Gibbs sampler. Applied Statistics, 43, 201–221.MATHCrossRef Wakefield, J., Smith, A., Racine-Poon, A., & Gelfand, A. (1994). Bayesian analysis of linear and non-linear population models using the Gibbs sampler. Applied Statistics43, 201–221.MATHCrossRef
Zurück zum Zitat Wakefield, J., Aarons, L., & Racine-Poon, A. (1999). The Bayesian approach to population pharmacokinetic/pharmacodynamic modelling. In C. Gatsonis, R. E. Kass, B. P. Carlin, A. L. Carriquiry, A. Gelman, I. Verdinelli, & M. West (Eds.), Case studies in Bayesian statistics, volume IV (pp. 205–265). New York: Springer.CrossRef Wakefield, J., Aarons, L., & Racine-Poon, A. (1999). The Bayesian approach to population pharmacokinetic/pharmacodynamic modelling. In C. Gatsonis, R. E. Kass, B. P. Carlin, A. L. Carriquiry, A. Gelman, I. Verdinelli, & M. West (Eds.), Case studies in Bayesian statistics, volume IV (pp. 205–265). New York: Springer.CrossRef
Zurück zum Zitat Wald, A. (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Transactions of the American Mathematical Society, 54, 426–482.MathSciNetMATHCrossRef Wald, A. (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Transactions of the American Mathematical Society54, 426–482.MathSciNetMATHCrossRef
Zurück zum Zitat Wand, M., & Jones, M. (1995). Kernel smoothing. Boca Raton: Chapman and Hall/CRC.MATH Wand, M., & Jones, M. (1995). Kernel smoothing. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Wand, M., & Ormerod, J. (2008). On semiparametric regression with O’Sullivan penalised splines. Australian and New Zealand Journal of Statistics, 50, 179–198.MathSciNetMATHCrossRef Wand, M., & Ormerod, J. (2008). On semiparametric regression with O’Sullivan penalised splines. Australian and New Zealand Journal of Statistics50, 179–198.MathSciNetMATHCrossRef
Zurück zum Zitat Watson, G. (1964). Smooth regression analysis. Sankhya, A26, 359–372. Watson, G. (1964). Smooth regression analysis. SankhyaA26, 359–372.
Zurück zum Zitat Wedderburn, R. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika, 61, 439–447.MathSciNetMATH Wedderburn, R. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika61, 439–447.MathSciNetMATH
Zurück zum Zitat Wedderburn, R. (1976). On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models. Biometrika, 63, 27–32.MathSciNetMATHCrossRef Wedderburn, R. (1976). On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models. Biometrika63, 27–32.MathSciNetMATHCrossRef
Zurück zum Zitat West, M. (1993). Approximating posterior distributions by mixtures. Journal of the Royal Statistical Society, Series B, 55, 409–422.MATH West, M. (1993). Approximating posterior distributions by mixtures. Journal of the Royal Statistical Society, Series B55, 409–422.MATH
Zurück zum Zitat West, M., & Harrison, J. (1997). Bayesian forecasting and dynamic models (2nd ed.). New York: Springer.MATH West, M., & Harrison, J. (1997). Bayesian forecasting and dynamic models (2nd ed.). New York: Springer.MATH
Zurück zum Zitat Westfall, P., Johnson, W., & Utts, J. (1995). A Bayesian perspective on the Bonferroni adjustment. Biometrika, 84, 419–427.MathSciNetCrossRef Westfall, P., Johnson, W., & Utts, J. (1995). A Bayesian perspective on the Bonferroni adjustment. Biometrika84, 419–427.MathSciNetCrossRef
Zurück zum Zitat White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 1721–746. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica48, 1721–746.
Zurück zum Zitat White, J. (1982). A two stage design for the study of the relationship between a rare exposure and a rare disease. American Journal of Epidemiology, 115, 119–128. White, J. (1982). A two stage design for the study of the relationship between a rare exposure and a rare disease. American Journal of Epidemiology115, 119–128.
Zurück zum Zitat Wood, S. (2006). Generalized additive models: An introduction with R. Boca Raton: Chapman and Hall/CRC.MATH Wood, S. (2006). Generalized additive models: An introduction with R. Boca Raton: Chapman and Hall/CRC.MATH
Zurück zum Zitat Wood, S. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society, Series B, 70, 495–518.MATHCrossRef Wood, S. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society, Series B70, 495–518.MATHCrossRef
Zurück zum Zitat Wood, S. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society, Series B, 73, 3–36.CrossRef Wood, S. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society, Series B, 73, 3–36.CrossRef
Zurück zum Zitat Wu, T., & Lange, K. (2008). Coordinate descent algorithms for lasso penalized regression. The Annals of Applied Statistics, 2, 224–244.MathSciNetMATHCrossRef Wu, T., & Lange, K. (2008). Coordinate descent algorithms for lasso penalized regression. The Annals of Applied Statistics2, 224–244.MathSciNetMATHCrossRef
Zurück zum Zitat Yates, F. (1984). Tests of significance for 2 ×2 contingency tables. Journal of the Royal Statistical Society, Series B, 147, 426–463.MathSciNetMATHCrossRef Yates, F. (1984). Tests of significance for 2 ×2 contingency tables. Journal of the Royal Statistical Society, Series B147, 426–463.MathSciNetMATHCrossRef
Zurück zum Zitat Yee, T., & Wild, C. (1996). Vector generalized additive models. Journal of the Royal Statistical Society, Series B, 58, 481–493.MathSciNetMATH Yee, T., & Wild, C. (1996). Vector generalized additive models. Journal of the Royal Statistical Society, Series B58, 481–493.MathSciNetMATH
Zurück zum Zitat Yu, K., & Jones, M. (2004). Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association, 99, 139–144.MathSciNetMATHCrossRef Yu, K., & Jones, M. (2004). Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association99, 139–144.MathSciNetMATHCrossRef
Zurück zum Zitat Yuan, M., & Lin, Y. (2007). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68, 49–67.MathSciNet Yuan, M., & Lin, Y. (2007). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B68, 49–67.MathSciNet
Zurück zum Zitat Zeger, S., & Liang, K. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121–130.CrossRef Zeger, S., & Liang, K. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics42, 121–130.CrossRef
Zurück zum Zitat Zhao, L., & Prentice, R. (1990). Correlated binary regression using a generalized quadratic model. Biometrika, 77, 642–648.MathSciNetCrossRef Zhao, L., & Prentice, R. (1990). Correlated binary regression using a generalized quadratic model. Biometrika77, 642–648.MathSciNetCrossRef
Zurück zum Zitat Zhao, L., Prentice, R., & Self, S. (1992). Multivariate mean parameter estimation by using a partly exponential model. Journal of the Royal Statistical Society, Series B, 54, 805–811. Zhao, L., Prentice, R., & Self, S. (1992). Multivariate mean parameter estimation by using a partly exponential model. Journal of the Royal Statistical Society, Series B54, 805–811.
Zurück zum Zitat Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67, 301–320.MathSciNetMATHCrossRef Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B67, 301–320.MathSciNetMATHCrossRef
Metadaten
Titel
General Regression Models
verfasst von
Jon Wakefield
Copyright-Jahr
2013
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4419-0925-1_6

Premium Partner