Consistency and Asymptotic Normality of the Maximum Quasi-likelihood Estimator in Quasi-likelihood Nonlinear Models with Random Regressors  

作　　者：Tian Xia Shun-fang Wang Xue-ren Wang 

机构地区：[1]Department of Statistics, Yunnan University, Kunming 650091, China [2]School of Information Science and Engineering, Yunnan University, Kunming 650091, China

出　　处：《Acta Mathematicae Applicatae Sinica》2010年第2期241-250,共10页应用数学学报（英文版）

基　　金：Supported by National Natural Science Foundation of China (No. 10761011,10671139,10901135);Natural Science Foundation of Yunnan Province(No. 2008CD081);Special Foundation for Middle and Young Excellent Teachers of Yunnan University

摘　　要：This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.

关 键 词：Asymptotic normality CONSISTENCY maximum quasi-likelihood estimator quasi-likelihood nonlinear models with random regressors 

分 类 号：O212.1[理学—概率论与数理统计] O212[理学—数学]