Semiparametric Likelihood-based Inference for Censored Data with Auxiliary Information from External Massive Data Sources

作     者：Yue-xin FANG Yong ZHOU Yue-xin FANG;Yong ZHOU

作者机构：School of Statistics and ManagementShanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and Data ScienceMOEand Academy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062China 

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

年 卷 期：2020年第36卷第3期

页      面：642-656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：supported by the State Key Program of National Natural Science Foundation of China(No.71331006) by the Graduate Innovation Foundation of Shanghai University of Finance and Economics of China(No.CXJJ-2018-408) 

主　　题：Auxiliary information Massive data Censored data Empirical likelihood Estimation equations 

摘      要：Published auxiliary information can be helpful in conducting statistical inference in a new *** this paper,we synthesize the auxiliary information with semiparametric likelihood-based inference for censoring data with the total sample size is *** express the auxiliary information as constraints on the regression coefficients and the covariate distribution,then use empirical likelihood method for general estimating equations to improve the efficiency of the interested parameters in the specified *** consistency and asymptotic normality of the resulting regression parameter estimators *** numerical simulation and application with different supposed conditions show that the proposed method yields a substantial gain in efficiency of the interested parameters.