EMPIRICAL LIKELIHOOD‐BASED INFERENCES FOR PARTIALLY LINEAR MODELS WITH MISSING COVARIATES |
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Authors: | Hua Liang Yongsong Qin |
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Institution: | 1. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA. e‐mail:;2. School of Mathematical Sciences, Guangxi Normal University, Guilin, Guangxi 541004, China. |
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Abstract: | This paper considers statistical inference for partially linear models Y = X ? β +ν(Z) +? when the linear covariate X is missing with missing probability π depending upon (Y, Z). We propose empirical likelihood‐based statistics to construct confidence regions for β and ν(z). The resulting empirical likelihood ratio statistics are shown to be asymptotically chi‐squared‐distributed. The finite‐sample performance of the proposed statistics is assessed by simulation experiments. The proposed methods are applied to a dataset from an AIDS clinical trial. |
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Keywords: | confidence region local linear regression missing at random semiparametric estimation |
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