Empirical Likelihood-based Inferences in Varying Coefficient Models with Missing Data  

作　　者：Xiao-hui LIU 

机构地区：[1]School of Statistics, Jiangxi University of Finance and Economics [2]Research Center of Applied Statistics, Jiangxi University of Finance and Economics

出　　处：《Acta Mathematicae Applicatae Sinica》2015年第3期823-840,共18页应用数学学报（英文版）

基　　金：supported in part by NSF of China(No.11461029);NSF of Jiangxi Province(No.20142BAB211014);YSFP of Jiangxi provincial education department(No.GJJ14350)

摘　　要：In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X^τα（U） ＋ ε when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α（u）. The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically.Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X^τα（U） ＋ ε when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α（u）. The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically.Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.

关 键 词：varying coefficient models missing at random empirical likelihood maximum empirical likelihood estimator 

分 类 号：O212.1[理学—概率论与数理统计]