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

作　　者：Yue-xin FANG Yong ZHOU 

机构地区：[1]School of Statistics and Management,Shanghai University of Finance and Economics,Shanghai,200433,China [2]Key Laboratory of Advanced Theory and Application in Statistics and Data Science,MOE,and Academy of Statistics and Interdisciplinary Sciences,East China Normal University,Shanghai 200062,China

出　　处：《Acta Mathematicae Applicatae Sinica》2020年第3期642-656,共15页应用数学学报（英文版）

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)。

摘　　要：Published auxiliary information can be helpful in conducting statistical inference in a new study.In this paper,we synthesize the auxiliary information with semiparametric likelihood-based inference for censoring data with the total sample size is available.We 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 model.The consistency and asymptotic normality of the resulting regression parameter estimators established.Also numerical simulation and application with different supposed conditions show that the proposed method yields a substantial gain in efficiency of the interested parameters.

关 键 词：Auxiliary information Massive data Censored data Empirical likelihood Estimation equations 

分 类 号：O212.1[理学—概率论与数理统计]