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- W2000838071 abstract "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 the asymptotic variance estimated consistently by the usual plug-in method. Moreover, the proposed estimators are more efficient than the weighted estimators with the inverse of true non-missingness probability as weight. Finite sample performance of the estimators is examined via simulation and a real dataset is analyzed to illustrate the proposed methods." @default.
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- W2000838071 date "2010-10-01" @default.
- W2000838071 modified "2023-09-27" @default.
- W2000838071 title "Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates" @default.
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- W2000838071 doi "https://doi.org/10.1016/j.jmva.2010.02.008" @default.
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