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Linear Regression Models with Incomplete Categorical Covariates

Author

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  • Helge Toutenburg

    (Ludwig-Maximilians-Universität München)

  • Thomas Nittner

    (Ludwig-Maximilians-Universität München)

Abstract

Summary We present three different methods based on the conditional mean imputation when binary explanatory variables are incomplete. Apart from the single imputation and multiple imputation especially the so-called pi imputation is presented as a new procedure. Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi imputation, single imputation, multiple imputation, modified first order regression. After a brief theoretical description of the simulation experiment, MSE-ratio, variance and bias are used to illustrate differences within and between the approaches.

Suggested Citation

  • Helge Toutenburg & Thomas Nittner, 2002. "Linear Regression Models with Incomplete Categorical Covariates," Computational Statistics, Springer, vol. 17(2), pages 215-232, July.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200103
    DOI: 10.1007/s001800200103
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    Cited by:

    1. Yeh Jason Jia-Hsing, 2009. "Missing (Completely?) At Random: Lessons from Insurance Studies," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 3(2), pages 1-13, April.
    2. Göran Kauermann & Mehboob Ali, 2021. "Semi-parametric regression when some (expensive) covariates are missing by design," Statistical Papers, Springer, vol. 62(4), pages 1675-1696, August.

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