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 

机构地区：[1]School of Mathematics and Statistics,Wuhan University

出　　处：《Acta Mathematicae Applicatae Sinica》2014年第3期663-676,共14页应用数学学报（英文版）

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)

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.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.

关 键 词：biased-sampling two-stage design empirical likelihood generalized linear models large-sample properties. 

分 类 号：O212.7[理学—概率论与数理统计] O212[理学—数学]