Cole, Bernard F.; Lee, Mei-Ling T.; Whitmore, G. Alex; Zaslavsky, Alan M. An empirical Bayes model for Markov-dependent binary sequences with randomly missing observations. (English) Zbl 0868.62017 J. Am. Stat. Assoc. 90, No. 432, 1364-1372 (1995). Summary: We develop an improved empirical Bayes estimation methodology for the analysis of two-state Markov chains observed from heterogeneous individuals. First, the two transition probabilities corresponding to each chain are assumed to be drawn from a common, bivariate distribution that has beta marginals. Second, randomly missing observations are incorporated into the likelihood for the hyperparameters by efficiently summing over all possible values for the missing observations. A likelihood ratio test is used to test for dependence between the transition probabilities. Posterior distributions for the transition probabilities are also derived, as is an approximation for the equilibrium probabilities. The proposed procedures are illustrated in a numerical example and in an analysis of longitudinal store display data. Cited in 3 Documents MSC: 62C12 Empirical decision procedures; empirical Bayes procedures 62M05 Markov processes: estimation; hidden Markov models Keywords:bivariate beta priors; dependent Bernoulli trials; marginal likelihood; maximum likelihood estimation; posterior distributions; empirical Bayes estimation; two-state Markov chains; transition probabilities; beta marginals; randomly missing observations; hyperparameters; likelihood ratio test; test for dependence; approximation; equilibrium probabilities PDFBibTeX XMLCite \textit{B. F. Cole} et al., J. Am. Stat. Assoc. 90, No. 432, 1364--1372 (1995; Zbl 0868.62017) Full Text: DOI