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Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates
2010
Journal of Multivariate Analysis
Missing covariate data are very common in regression analysis. In this paper, the weighted estimating equation method (Qi et al., 2005) [25] is used to extend the so-called unified estimation procedure (Chen et al., 2002) [4] for linear transformation models to the case of missing covariates. The non-missingness probability is estimated nonparametrically by the kernel smoothing technique. Under missing at random, the proposed estimators are shown to be consistent and asymptotically normal, with
doi:10.1016/j.jmva.2010.02.008
fatcat:saroxmt2dzd53p5teja4j4hyou