Statistical Inference in Partially Linear Varying-Coefficient Models with Missing Responses at Random

Abstract

Abstract This article considers statistical inference for partially linear varying-coefficient models when the responses are missing at random. We propose a profile least-squares estimator for the parametric component with complete-case data and show that the resulting estimator is asymptotically normal. To avoid to estimate the asymptotic covariance in establishing confidence region of the parametric component with the normal-approximation method, we define an empirical likelihood based statistic and show that its limiting distribution is chi-squared distribution. Then, the confidence regions of the parametric component with asymptotically correct coverage probabilities can be constructed by the result. To check the validity of the linear constraints on the parametric component, we construct a modified generalized likelihood ratio test statistic and demonstrate that it follows asymptotically chi-squared distribution under the null hypothesis. Then, we extend the generalized likelihood ratio technique to the context of missing data. Finally, some simulations are conducted to illustrate the proposed methods. Keywords: Complete-case dataEmpirical likelihoodMissing at randomPartially linear modelProfile least-squares approachVarying-coefficientMathematics Subject Classification: 62G0562G10 Acknowledgments The research was supported by the National Social Science Foundation of China (No. 07CTJ003), and "211 Project" Foundation of Minzu University of China (No. 021211030312).