数学物理学报

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广义线性回归极大似然估计的强相合性

丁洁丽;陈希孺   

  1. 武汉大学数学与统计学院;中国科学院研究生院
  • 收稿日期:2003-11-11 修回日期:2004-12-19 出版日期:2006-04-25 发布日期:2006-04-25
  • 通讯作者: 丁洁丽

Strong Consistency of the Maximum Likelihood Estimator in Generalized Linear Models

Ding Jieli;Chen Xiru   

  1. School of Mathematics and Statistics, Wuhan University;Graduate School, The Chinese Academy of Science
  • Received:2003-11-11 Revised:2004-12-19 Online:2006-04-25 Published:2006-04-25
  • Contact: Ding Jieli

摘要: 设有该文第1节所描述的广义线性回归模型,以$\underline{\lambda}_n$
和$\overline{\lambda}_n$分别记$\sum\limits_{i=1}^{n}Z_iZ_i^{\prime}$的最小和最大特征根,$\hat{\beta}_n$记$\beta_0$
的极大似然估计.在文献[1]中,当\{$Z_i,i\ge1$\}有界时得到$\hat{\beta}_n$强相合的充分条件,在自然联系和非自然联系下分别为
$\underline{\lambda}_n\rightarrow\infty$, $(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$
(对某$\delta>0$)以及$\underline{\lambda}_n\rightarrow\infty$, $\overline{\lambda}_n=O(\underline{\lambda}_n)$.
作者将后一结果改进为只要求$(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$,从而与自然联系情况下的
条件达到一致.

关键词: 广义线性模型, 极大似然估计, 强相合性

Abstract: Assuming the generalized linear model as described in \S1, let $\underline{\lambda}_n$and $\overline{\lambda}_n$ denote the minimum and maximum eigentvalues of$\sum\limits_{i=1}^{n}Z_iZ_i^{\prime}$ resp., and$\hat{\beta}_n$ denote the maximum likelihood estimator of $\beta_0$. It is shown in [1] that, when \{$Z_i,i\ge1$\}is bounded, the sufficient conditions for strong consistency of $\hat{\beta}_n$ are as follows:$\underline{\lambda}_n\rightarrow\infty$, $(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$(for some $\delta>0$) with natural link function, and $\underline{\lambda}_n\rightarrow\infty$,$\overline{\lambda}_n=O(\underline{\lambda}_n)$ with nonnatural link function resp.. In this paper, the authors improvethe latter result by showing that even in the case of nonnatural link function, the condition$(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$ remains to be sufficient.

Key words: Generalized linear model, Maximum likelihood estimate, Strong consistency.

中图分类号: 

  • 62J12