Statistical inference methods and applications of outcome-dependent sampling designs under generalized linear models

作     者：YAN Shu DING JieLi LIU YanYan 

作者机构：School of Mathematics and Statistics Wuhan University Wuhan 430072 China 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2017年第60卷第7期

页      面：1219-1238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：National Natural Science Foundation of China(Grant Nos. 11571263 11371299 and 11101314) 

主　　题：biased-sampling two-phase design generalized linear models empirical likelihood 

摘      要：A cost-effective sampling design is desirable in large cohort studies with a limited budget due to the high cost of measurements of primary exposure *** outcome-dependent sampling(ODS) designs enrich the observed sample by oversampling the regions of the underlying population that convey the most information about the exposure-response *** generalized linear models(GLMs) are widely used in many fields,however,much less developments have been done with the GLMs for data from the ODS *** study how to fit the GLMs to data obtained by the original ODS design and the two-phase ODS design,*** asymptotic properties of the proposed estimators are derived.A series of simulations are conducted to assess the finite-sample performance of the proposed *** to a Wilms tumor study and an air quality study demonstrate the practicability of the proposed methods.