Linear Regression Models with Incomplete Categorical Covariates

Helge Toutenburg and Thomas Nittner
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Helge Toutenburg: Ludwig-Maximilians-Universität München
Thomas Nittner: Ludwig-Maximilians-Universität München

Computational Statistics, 2002, vol. 17, issue 2, No 5, 215-232

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.

Keywords: binary variables; imputation; incomplete data; logistic regression; simulation experiment (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1007/s001800200103

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