New aspects of statistical methods for missing data problems, with applications in bioinformatics and genetics

We propose a nonparametric imputation procedure for data with missing val­ ues and establish empirical likelihood based inference for parameters defined by general estimating equations. The imputation is carried out multiple times via a nonparamet­ ric estimator of the conditional distribution of the missing component given the always observable component of the random vector under study. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can correct the selection bias in the missingness and empirical likelihood leads to more efficient parameter estimation. The proposed method is evaluated by simulation and an empirical study on the relationship between eye weight and gene transcriptional abundance of recombinant inbred mice.

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