Asymptotic properties of the maximum likelihood estimate in generalized linear models with stochastic regressors

被引：6

作者：

Ding, Jie Li ^{[1
]}

Chen, Xi Ru

机构：

[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China

[2] Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China

来源：

ACTA MATHEMATICA SINICA-ENGLISH SERIES | 2006年 / 22卷 / 06期

关键词：

generalized linear models; consistency; asymptotic normality;

D O I：

10.1007/s10114-005-0693-3

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE) beta(n) of the parameter are studied. Under reasonable conditions, we prove the weak, strong consistency and asymptotic normality of beta(n).

引用

页码：1679 / 1686

页数：8

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