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

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Listed:
  • Jingheng Cai
  • Zhibin Liang
  • Rongqian Sun
  • Chenyi Liang
  • Junhao Pan

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.

Suggested Citation

  • Jingheng Cai & Zhibin Liang & Rongqian Sun & Chenyi Liang & Junhao Pan, 2019. "Bayesian analysis of latent Markov models with non-ignorable missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(13), pages 2299-2313, October.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2299-2313
    DOI: 10.1080/02664763.2019.1584162
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    Cited by:

    1. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.

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