Analysis of Panel Count Data with Time-dependent Covariates and Informative Observation Process

作     者：Sha FANG Hai-xiang ZHANG Liu-quan SUN De-hui WANG 

作者机构：School of Statistics Capital University of Economics and Business Beijing 100070 China Center for Applied Mathematics Tianjin University Tianjin 300072 China Institute of Applied Mathematics Chinese Academy of Sciences Beijing 100190 China Mathematics School of Jilin University Changchun 130012 China 

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

年 卷 期：2017年第33卷第1期

页      面：147-156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：partially supported by National Natural Science Foundation of China(11671267) Scientific Research Level Improvement Quota Project of Capital University of Economics and Business and Scientific Research Foundation for Young Teachers of Capital University of Economics and Business(00591654490336) partially supported by the National Natural Science Foundation of China(Nos.11301212,11401146) partially supported by the National Natural Science Foundation of China Grants(No.11231010,11171330 and 11021161) Key Laboratory of RCSDS,CAS(No.2008DP173182) partly supported by National Natural Science Foundation of China(11271155) Specialized Research Fund for the Doctoral Program of Higher Education(20110061110003) Scientific Research Fund of Jilin University(201100011) Jilin Province Natural Science Foundation(20101596) 

主　　题：estimating equation informative observation process joint modeling model checking panel countdata 

摘      要：Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with time-dependent covariates and possibly in the presence of informative observation process via two latent variables. For the inference on the proposed model, a class of estimating equations is developed and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is provided for assessing the adequacy of the model. The finite-sample behavior of the proposed methods is examined through Monte Carlo simulation studies which suggest that the proposed approach works well for practical situations. Also an illustrative example is provided.