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

Author:

Cai Jingheng1,Liang Zhibin1,Sun Rongqian1,Liang Chenyi1,Pan Junhao2

Affiliation:

1. Department of Statistics, Sun Yat-sen University, Guangzhou, People's Republic of China

2. Department of Psychology, Sun Yat-sen University, Guangzhou, People's Republic of China

Funder

National Natural Science Foundation of China

MOE (Ministry of Education) Project of Humanities and Social Science of China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference37 articles.

1. H. Akaike, Information theory and an extension of the maximum likelihood principle, in Second International Symposium on Information Theory, B.N. Petrox and F. Caski, eds., Akademiai Kiado, Budapest, 1973, pp. 267–281

2. A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates

3. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

4. Bureau of Labor Statistics, U.S. Department of Labor, National Longitudinal Survey of Youth 1997 cohort, The National Opinion Research Center, the University of Chicago and distributed by the Center for Human Resource Research, The Ohio State University, Columbus, 2015.

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1. Constructing high frequency economic indicators by imputation;The Econometrics Journal;2023-11-15

2. Bayesian analysis of mixture models with Yeo-Johnson transformation;Communications in Statistics - Theory and Methods;2023-08-24

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