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An empirical likelihood method in a partially linear single-index model with right censored data
Authors:Yi Ping Yang  Liu Gen Xue  Wei Hu Cheng
Institution:(1) College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, 400067, P. R. China;(2) College of Applied Sciences, Beijing University of Technology, Beijing, 100124, P. R. China
Abstract:Empirical-likelihood-based inference for the parameters in a partially linear single-index model with randomly censored data is investigated. We introduce an estimated empirical likelihood for the parameters using a synthetic data approach and show that its limiting distribution is a mixture of central chi-squared distribution. To attack this difficulty we propose an adjusted empirical likelihood to achieve the standard χ 2-limit. Furthermore, since the index is of norm 1, we use this constraint to reduce the dimension of parameters, which increases the accuracy of the confidence regions. A simulation study is carried out to compare its finite-sample properties with the existing method. An application to a real data set is illustrated.
Keywords:Censored data  regression model  empirical likelihood  x2-distribution
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