Semiparametric Empirical Likelihood Estimation for Two-stage Outcome-dependent Sampling under the Frame of Generalized Linear Models

作     者：Jie-li DING Yan-yan LIU 

作者机构：School of Mathematics and StatisticsWuhan University 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2014年第30卷第3期

页      面：663-676页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：Jie-li DING is supported by the National Natural Science Foundation of China(No.11101314) Yan-yan LIU s supported by the National Natural Science Foundation of China(No.11171263 No.11371299) 

主　　题：biased-sampling two-stage design empirical likelihood generalized linear models large-sample properties. 

摘      要：Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.