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

页码：2299 / 2313

页数：15

共 37 条

[21] Maximum likelihood estimation and model comparison for mixtures of structural equation models with ignorable missing data [J].

Lee, SY ;

Song, XY .

JOURNAL OF CLASSIFICATION, 2003, 20 (02) :221-255

[22]

Little R. J., 2019, STAT ANAL MISSING DA, V793

[23] Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application [J].

Lu, Zhenqiu ;

Zhang, Zhiyong .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2014, 71 :220-240

[24] Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data [J].

Lu, Zhenqiu Laura ;

Zhang, Zhiyong ;

Lubke, Gitta .

MULTIVARIATE BEHAVIORAL RESEARCH, 2011, 46 (04) :567-597

[25] Mixed hidden Markov quantile regression models for longitudinal data with possibly incomplete sequences [J].

Marino, Maria Francesca ;

Tzavidis, Nikos ;

Alfo, Marco .

STATISTICAL METHODS IN MEDICAL RESEARCH, 2018, 27 (07) :2231-2246

[26] Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition [J].

Marino, Maria Francesca ;

Alfo, Marco .

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2015, 9 (04) :483-502

[27] EQUATION OF STATE CALCULATIONS BY FAST COMPUTING MACHINES [J].

METROPOLIS, N ;

ROSENBLUTH, AW ;

ROSENBLUTH, MN ;

TELLER, AH ;

TELLER, E .

JOURNAL OF CHEMICAL PHYSICS, 1953, 21 (06) :1087-1092

[28]

Plummer M, 2006, R NEWS, V6, P7, DOI DOI 10.1159/000323281

[29] Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method [J].

Robert, CP ;

Rydén, T ;

Titterington, DM .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2000, 62 :57-75

[30] Hidden Markov models for longitudinal comparisons [J].

Scott, SL ;

James, GM ;

Sugar, CA .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2005, 100 (470) :359-369

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