Bayesian analysis of latent variable models with non-ignorable missing outcomes from exponential family

Statistics in Medicine
Xin-Yuan Song, Sik-Yum Lee


To provide a comprehensive framework for analysing complex non-normal medical and biological data, we propose a Bayesian approach for a non-linear latent variable model with covariates, and non-ignorable missing data, under the exponential family of distributions. The non-ignorable missing mechanism is defined via a logistic regression model. Based on conjugate prior distributions, full conditional distributions for the implementation of Markov chain Monte Carlo methods in simulating observations from the joint posterior distribution are derived. These observations are used in computing the Bayesian estimates, as well as in implementing a path sampling procedure to evaluate the Bayes factor for model comparison. The proposed methods are illustrated using real data from a study on the non-adherence of hypertension patients.


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Jun 3, 2004·The British Journal of Mathematical and Statistical Psychology·Sik-Yum Lee, Xin-Yuan Song
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Oct 5, 2013·Psychometrika·Xin-Yuan SongEdward Hak-Sing Ip
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Jun 1, 2009·Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·Joyee Ghosh, David B Dunson
Dec 2, 2008·The British Journal of Mathematical and Statistical Psychology·Ye-Mao XiaSik-Yum Lee
Jul 2, 2008·The British Journal of Mathematical and Statistical Psychology·Sik-Yum LeeChung-Ngor Juliana Chan
May 31, 2007·The British Journal of Mathematical and Statistical Psychology·Dimitris Rizopoulos, Irini Moustaki
Nov 30, 2019·Multivariate Behavioral Research·Hongyun LiuFang Liu

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