Bayesian analysis of latent Markov models with non-ignorable missing data

被引：2
|
作者：
Cai, Jingheng [1 ]
Liang, Zhibin [1 ]
Sun, Rongqian [1 ]
Liang, Chenyi [1 ]
Pan, Junhao [2 ]
机构：
[1] Sun Yat Sen Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Dept Psychol, Guangzhou, Guangdong, Peoples R China
来源：
JOURNAL OF APPLIED STATISTICS | 2019年 / 46卷 / 13期
基金：
中国国家自然科学基金;
关键词：
Latent Markov models; non-ignorable missing data; MCMC methods; complete DIC; STRUCTURAL EQUATION MODELS; GROWTH MIXTURE-MODELS; VARIABLE MODELS; DISTRIBUTIONS; INFERENCE; RESPONSES;
D O I：
10.1080/02664763.2019.1584162
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：
Latent Markov models (LMMs) are widely used in the analysis of heterogeneous longitudinal data. However, most existing LMMs are developed in fully observed data without missing entries. The main objective of this study is to develop a Bayesian approach for analyzing the LMMs with non-ignorable missing data. Bayesian methods for estimation and model comparison are discussed. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from National Longitudinal Survey of Youth 1997 is presented.
引用
收藏
页码：2299 / 2313
页数：15
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