Asymptotic normality and strong consistency of maximum quasi-likelihood estimates in generalized linear models

作     者：YIN Changming, ZHAO Lincheng & WEI Chengdong School of Mathematics and Information Science, Guangxi University, Manning 530004, China Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China Department of Mathematics, Guangxi Teacher College, Manning 530001, China 

作者机构：School of Mathematics and Information Science Guangxi University Nanning China Department of Statistics and Finance University of Science and Technology of China Hefei China Department of Mathematics Guangxi Teacher College Nanning China 

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

年 卷 期：2006年第49卷第2期

页      面：145-157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 National Natural Science Foundation of China(Grant No.10471136) Ph.D.Program Foundation of Ministry of Education of China and Special Foundation of the Chinese Academy of Science and USTC 

主　　题：generalized linear models quasi-likelihood estimates asymptotic normality strong consistency. 

摘      要：In a generalized linear model with q x 1 responses, the bounded and fixed (or adaptive) p × q regressors Zi and the general link function, under the most general assumption on the minimum eigenvalue of ZiZ i,the moment condition on responses as weak as possible and the other mild regular conditions, we prove that the maximum quasi-likelihood estimates for the regression parameter vector are asymptotically normal and strongly consistent.