Parameter estimation in a model for misclassified Markov data - a Bayesian approach.

Rosychuk and Islam have a paper in Computational Statistics and Data Analysis. This concerns parameter estimation in a two-state recurrent misclassification type hidden Markov model, where the Markov process is assumed to be continuous time and in equilibrium and is observed at discrete, equally spaced time points. A Bayesian approach to estimation is considered via Gibbs sampling. To avoid identifiability issues, the misclassification probabilities are constrained to be below 0.5. An additional issue is the choice of starting values of the transition probabilities for the latent Markov process. Values based on simple correction formulae previously developed by Rosychuk and Thompson appear to perform better than values based on taking naive estimates of the transition probabilities of the observed process.