Search Results

Title: Bayesian Analysis of Latent Markov Models with Non-ignorable Missing Data
Resulting in 1 citation.
1. Cai, Jingheng
Liang, Zhibin
Sun, Rongqian
Liang, Chenyi
Pan, Junhao
Bayesian Analysis of Latent Markov Models with Non-ignorable Missing Data
Journal of Applied Statistics published online (27 February 2019): DOI: 10.1080/02664763.2019.1584162.
Also: https://www.tandfonline.com/doi/full/10.1080/02664763.2019.1584162
Cohort(s): NLSY97
Publisher: Taylor & Francis Group
Keyword(s): Bayesian; Household Income; Modeling; Poverty

Permission to reprint the abstract has not been received from the publisher.

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.
Bibliography Citation
Cai, Jingheng, Zhibin Liang, Rongqian Sun, Chenyi Liang and Junhao Pan. "Bayesian Analysis of Latent Markov Models with Non-ignorable Missing Data." Journal of Applied Statistics published online (27 February 2019): DOI: 10.1080/02664763.2019.1584162.