Nonlinear mixed-effect models with nonignorably missing covariates

被引：8
|
作者：
Wu, L [1 ]
机构：
[1] Univ British Columbia, Dept Stat, Vancouver, BC V6T 1Z2, Canada
来源：
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2004年 / 32卷 / 01期
关键词：
EM algorithm; Gibbs sampling; longitudinal data; missing data; rejection sampling;
D O I：
10.2307/3315997
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：
Nonlinear mixed-effect models are often used in the analysis of longitudinal data. However, it sometimes happens that missing values for some of the model covariates are not purely random. Motivated by an application to HIV viral dynamics, where this situation occurs, the author considers likelihood inference for this type of problem. His approach involves a Monte Carlo EM algorithm, along with a Gibbs sampler and rejection/importance sampling methods. A concrete application is provided.
引用
收藏
页码：27 / 37
页数：11
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