A nonlinear Gauss-Seidel algorithm for inference about GLMM

被引：10

作者：

Jiang, JM ^{[1
]}

机构：

[1] Case Western Reserve Univ, Dept Stat, Cleveland, OH 44106 USA

来源：

COMPUTATIONAL STATISTICS | 2000年 / 15卷 / 02期

关键词：

maximum posterior; generalized linear mixed models; recursive algorithm; convergence;

D O I：

10.1007/s001800000030

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typical situations of generalized linear mixed models. A numerical example shows the superiority of the proposed algorithm over the standard Newton-Raphson procedure when the number of random effects is large.

引用

页码：229 / 241

页数：13

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