ON NONLINEAR RANDOM EFFECTS MODELS FOR REPEATED MEASUREMENTS

被引：37

作者：

HIRST, K ^{[1
]}

BOYLE, DW ^{[1
]}

ZERBE, GO ^{[1
]}

WILKENING, RB ^{[1
]}

机构：

[1] UNIV COLORADO,SCH MED,DENVER,CO 80206

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 1991年 / 20卷 / 2-3期

基金：

美国国家卫生研究院;

关键词：

NONLINEAR MIXED EFFECTS MODEL; EM ALGORITHM; LONGITUDINAL DATA; STOCHASTIC PARAMETERS;

D O I：

10.1080/03610919108812966

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：

Linear random effects models for longitudinal data discussed by Laird and Ware (1982), Jennrich and Schluchter (1986), Lange and Laird (1989), and others are extended in a straight forward manner to nonlinear random effects models. This results in a simple computational approach which accommodates patterned covariance matrices and data insufficient for fitting each subject separately. The technique is demonstrated with an interesting medical data set, and a short, simple SAS PROC IML program based on the EM algorithm is presented.

引用

页码：463 / 478

页数：16

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