Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Bayesian Hierarchical Models for Inference About Population Growth Kapitel lesen Erstes Kapitel lesen

2009 | OriginalPaper | Buchkapitel

Gaussian Semiparametric Analysis Using Hierarchical Predictive Models

verfasst von : Daniel Fink, Wesley Hochachka

Erschienen in: Modeling Demographic Processes In Marked Populations

Verlag: Springer US

Einloggen

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

search-config
loading …

Abstract

The Hierarchical Predictive Model (HPM) is a semiparametric mixed model where the fixed effects are fit with a user-specified non-parametric component. This approach extends current spline-based semiparametric mixed model formulations, allowing for more flexible nonparametric estimation. Greater adaptability simplifies model specification making it easier to analyze data sets with large numbers of predictors. Greater automation also extends the scope of exploratory analyses that may be performed with mixed models. Using a HPM, the analyst may select the predictive model to best suit their needs, exploiting the strengths of currently available predictive methods. A simulation study is used to demonstrate the advantages of accounting for known hierarchical structure in predictive models and to illustrate the adaptability of current decision-tree based predictive models. A HPM of the relative abundance of the North American House Finch (Carpodacus mexicanus) is used to demonstrate exploratory analysis with a real data set.

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel A Spatial Model for Estimating Mortality Rates, Abundance and Movement Probabilities from Fishery Tag-Recovery Data
Nächstes Kapitel Effect of Senescence on Estimation of Survival Probability When Age Is Unknown
Literatur
Amstrup S, MacDonald L, Manly B (2006) Handbook of Rapture-Recapture Analysis. Princeton University Press, Englewood Cliffs, NJ 296 pp.
Banerjee S, Carlin BP, Gelfand AE (2004) Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, London BocaRadon, FL 472 pp.
Berry S, Carroll R, Ruppert D (2002) Bayesian smoothing and regression splines for measurement error problems. Journal of the American Statistical Association 97:160–169.CrossRefMATHMathSciNet
Breiman L, Friedman JH, Olshen RA, Stone JC (1984) Classification and Regression Trees. Chapman & Hall, New York.
Carlin BP, Louis TA (2000) Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall/CRC, Boca Raton, FL.CrossRef
Chatfield C (1995) Model uncertainty, data mining, and statistical inference. Journal of the Royal Statistical Society, Series A, 158:419–466.CrossRef
Cristianini N, Shawe-Taylor J (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK.CrossRef
De'ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192.CrossRef
Dhondt AA, Altizer S, Cooch EG, Davis AK, Dobson A, Driscoll MJL, Hartup BK, Hawley DM, Hochachka WM, Hosseini PR, Jennelle CS, Kollias GV, Ley DH, Swarthout ECH, Sydenstricker KV (2005) Dynamics of a novel pathogen in an avian host: mycoplasmal conjunctivitis in House Finches. Acta Tropica 94(1):77–93.CrossRef
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz G, Nakamura M, Nakazawa Y, Overton JMcC, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129–151.CrossRef
Friedman JH, Popescu BE (2005) Predictive learning via rule ensembles. Technical Report, Stanford University.
Geissler PH, Sauer JR (1990) Topics in route-regression analysis. In: Sauer JR, Droege S (eds) Survey Designs and Statistical Methods for the Estimation of Avian Population Trends. U.S. Fish and Wildlife Service, Biological Report 90(1):54–57.
Gelfand A, Schmidt AM, Wu S, Silander JA, Latimer A, Rebelo AG (2005) Modelling species diversity through species level hierarchical modeling. Applied Statistics, 54:1–20.MATHMathSciNet
Goldstein H (1995) Multilevel Statistical Models. Halstead Press, New York..
Hand DJ, Mannila H, Smyth P (2001) Principles of Data Mining. MIT Press, Cambridge.
Hastie T, Tibshirani R (1990) Generalized Additive Models. Chapman and Hall, London.
Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Verlag, New York, 552 pp.CrossRef
Hochachka WM, Dhondt AA (2000) Density-dependent decline of host abundance resulting from a new infectious disease. Proceedings of the National Academy of Sciences USA 97:5303–5306.CrossRef
Hochachka WM, Dhondt AA (2006) House finch (Carpodacus mexicanus) population- and group-level responses to a bacterial disease. Ornithological Monographs 60:30–43.CrossRef
Hochachka WM, Caruana R, Fink D, Kelling S, Munson A, Riedewald M, Sorokina D (2007) Data mining for discovery of pattern and process in ecological systems. Journal of Wildlife Management 71(7):2427–2437.
Hooker G (2007) Generalized functional ANOVA diagnostics for high dimensional functions of dependent variables. Journal of Computational and Graphical Statistics 16(3).
Jolly GM (1965) Explicit estimates from capture-recapture data with both death and immigration-stochastic models. Biometrika 52:225–247.MATHMathSciNet
Lepage D, Francis CM (2002) Do feeder counts reliably indicate bird population changes? 21 years of winter bird counts in Ontario, Canada. Condor 104:255–270.
Lehmann E, Casella G (1999) Theory of Point Estimation, 2nd Edition. Springer-Verlag, New York.
Lindley DV, Smith AFM (1972) Bayes estimates for the linear model. Journal of the Royal Statistical Society, Series B, 1–41.
Mackenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8):2248–2255.CrossRef
McCulloch CE, Searle SR (2001) Generalized, Linear, and Mixed Models. John Wiley and Sons, New York.
Mitchell T (1997) Machine Learning. McGraw-Hill, New York.
Raftery AE, Lewis SM (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science 7:493–497.CrossRef
R Development Core Team (2006). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://​www.​R-project.​org.
Robert CP, Casella G (2004) Monte Carlo Statistical Methods, 2nd Edition. Springer, New York.CrossRef
Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Statistical Science 8(1):15–51.CrossRef
Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric Regression. Cambridge University Press, Cambridge, 402 pp.CrossRef
Thogmartin WE, Sauer JR, Knutson MG (2004) A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecological Applications 14(6):1766–1779.CrossRef
Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58(1):267–288.MATHMathSciNet
Wahba G (1990) Spline models for observational data SIAM [Society for Industrial and Applied Mathematics] (Philadelphia).
Wells JV, Rosenberg KV, Dunn EH, Tessaglia-Hymes DL, Dhondt AA (1998) Feeder counts as indicators of spatial and temporal variation in winter abundance of resident birds. Journal of Field Ornithology 69:577–586.
West M, Harrison J (1997) Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York.
Wikle CK (2003) Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84:1382–1394.CrossRef
Wikle CK, Hooten MB (2006) Hierarchical bayesian spatio-temporal models for population spread. In: Clark JS and Gelfand A (eds) Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods. Oxford University Press, Oxford.
Wood SN (2006) Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, London/Boca Rarm, FL, 416 pp.
Zhao X, Wells MT (2005) Reference priors for linear models with general covariance structures, Cornell Department of Statistical Sciences Technical Report.
Zhao Y, Staudenmayer J, Coull BA, Wand MP (2006) General design Bayesian generalized linear mixed models. Statistical Science 21:35–51.CrossRefMATHMathSciNet
Metadaten
Titel
Gaussian Semiparametric Analysis Using Hierarchical Predictive Models
verfasst von
Daniel Fink
Wesley Hochachka
Copyright-Jahr
2009
Verlag
Springer US
Buch
Modeling Demographic Processes In Marked Populations

Print ISBN: 978-0-387-78150-1

Electronic ISBN: 978-0-387-78151-8

Copyright-Jahr: 2009

DOI
https://doi.org/10.1007/978-0-387-78151-8_46

Premium Partner

    Bildnachweise