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2020 | OriginalPaper | Buchkapitel

3. Missing Data Methods

verfasst von : Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess

Erschienen in: Applied Multiple Imputation

Verlag: Springer International Publishing

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Abstract

In this chapter missing data procedures and techniques are reviewed and discussed. Among them are both, ad-hoc methods but also more sophisticated techniques including maximum likelihood estimation, weighting and imputation. We discuss pros and cons of the different approaches and techniques, and give practical advice which procedure might be suited best in a given scenario because valid inferences in applied research can only be expected based on informed decisions. A conclusion of this chapter will be that there is not the one method or technique that works best under every possible scenario.

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Vorheriges Kapitel Missing Data Mechanism and Ignorability

Nächstes Kapitel Multiple Imputation: Theory

Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge, UK: University Press.MATHCrossRef

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), 1–22.MathSciNetMATH

Freedman, D. A., & Berk, R. A. (2008). Weighting regressions by propensity scores. Evaluation Review, 32(4), 392–409.CrossRef

Hausman, J. A., & Wise, D. A. (1979). Attrition bias in experimental and panel data: The Gary income maintenance experiment. Econometrica, 47(2), 455–473.CrossRef

Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.MathSciNetMATHCrossRef

Horvitz, D. G. & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47 (260), 663–685.MathSciNetMATHCrossRef

Jamshidian, M., & Jennrich, R. I. (2000). Standard errors for EM estimation. Journal of the Royal Statistical Society, Series B, 62(2), 257–270.MathSciNetCrossRef

Jones, M. P. (1996). Indicator and stratification methods for missing explanatory variables in multiple linear regression. Journal of the American Association, 91(433), 222–230.MathSciNetMATHCrossRef

Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523–539.MathSciNetMATHCrossRef

Lehmann, E. L. (1998). Elements of large-sample theory. New York, NY: Springer.

Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed.). New York, NY: Springer.MATH

Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88(421), 125–134.MATH

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New Jersey, NJ: Wiley.MATHCrossRef

Liu, C., & Rubin, D. B. (1994). The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence. Biometrika, 81(4), 633–648.MathSciNetMATHCrossRef

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403–425.CrossRef

Meng, X.-L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9(4), 538–558.CrossRef

Meng, X.-L., & Rubin, D. B. (1991). Using EM to obtain asymptotic variance-covariance matrices: The SEM algorithm. Journal of the American Statistical Association, 86(416), 899–909.CrossRef

Meng, X.-L., & Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80(2), 267–278.MathSciNetMATHCrossRef

Meng, X.-L., & van Dyk, D. (1997). The EM algorithm—an old folk-song sung to a fast new tune. Journal of the Royal Statistical Society, 59(3), 511–540.MathSciNetMATHCrossRef

Newey, W. K., & McFadden, D. L. (1994). Large sample estimation and hypothesis testing. In R. F. Engle & D. L. McFadden (Eds.), Handbook of econometrics (Vol. IV, pp. 2111–2245). Amsterdam: Elsevier.

Oakes, D. (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society, Series B, 61(2), 479–482.MathSciNetMATHCrossRef

Pagan, A., & Ullah, A. (1999). Nonparametric econometrics. Cambridge, UK: Cambridge University Press.CrossRef

Ridder, G. (1990). Attrition in multi-wave panel data. In J. Hartog, G. Ridder, & J. Theeuwes (Eds.), Panel data and labor market studies (pp. 45–67). Amsterdam: Elsevier.

Robins, J. M., Rotnitzky, A., & Zhao, L. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90(429), 106–121.MathSciNetMATHCrossRef

Robins, J. M., & Wang, N. (2000). Inference for imputation estimators. Biometrika, 87(1), 113–124.MathSciNetMATH

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.MATHCrossRef

Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91(434), 473–489.MATHCrossRef

Särndal, C.-E., Swensson, B., & Wretman, J. (1992). Model assisted survey sampling. New York, NY: Springer.MATHCrossRef

Schafer, J. L. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica, 57(1), 19–35.MathSciNetCrossRef

Serfling, R. J. (1980). Approximation theorems of mathematical statistics. New York, NY: Wiley.MATHCrossRef

Spanos, A. (1995). On normality and the linear regression model. Econometric Reviews, 14(2), 195–203.MathSciNetMATHCrossRef

Spiess, M., & Kroh, M. (2010). A selection model for panel data: The prospects of green party support. Political Analysis, 18(2), 172–188.CrossRef

Tsiatis, A. A. (2006). Semiparametric theory and missing data. New York, NY: Springer.MATH

Vella, F. (1998). Estimating models with sample selection bias: A survey. The Journal of Human Resources, 33(1), 127–169.CrossRef

Wang, N., & Robins, J. M. (1998). Large-sample theory for parametric multiple imputation procedures. Biometrika, 85(4), 935–948.MathSciNetMATHCrossRef

Wooldridge, J. M. (2002a). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.MATH

Wooldridge, J. M. (2002b). Inverse probability weighted M-estimators for sample selection, attrition and stratification. Portuguese Economic Journal, 1(2), 117–139.CrossRef

Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of Econometrics, 141(2), 1281–1301.MathSciNetMATHCrossRef

Titel: Missing Data Methods
verfasst von: Kristian Kleinke
Jost Reinecke
Daniel Salfrán
Martin Spiess
Verlag: Springer International Publishing
Buch: Applied Multiple Imputation
Print ISBN: 978-3-030-38163-9

Electronic ISBN: 978-3-030-38164-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-38164-6_3

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