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

Jingheng Cai, Zhibin Liang, Rongqian Sun, Chenyi Liang and Junhao Pan

Journal of Applied Statistics, 2019, vol. 46, issue 13, 2299-2313

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:46:y:2019:i:13:p:2299-2313

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DOI: 10.1080/02664763.2019.1584162

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