Statistical Inference about Markov Chains

Bayesian inference for Markov chains ∗

We consider the estimation of Markov transition matrices by Bayes’ methods. We obtain large and moderate deviation principles for the sequence of Bayesian posterior distributions. MSC 2000 subject classification: 60F10, 62M05

Inference about stationary distributions of Markov chains based on divergences with observed frequencies

For data generated by stationary Markov chains there are considered estimates of chain parameters minimizing ^-divergences between theoretical and empirical distributions of states. Consistency and asymptotic normality are established and the asymptotic covariance matrices are evaluated. Testing of hypotheses about the stationary distributions based on (^-divergences between the estimated and e...

Markov Chain Monte Carlo for Statistical Inference

Statistical Inference : in Markov - Renewal Processes

The undesirable prospect of increasing congestion un the nation'sairways has caused attention to be focused more and more on terminalareas. Two distinct efforts to improve traffic flow have been studiedelsewhere. One, by means of careful control of aircraft separationalong flight paths leading to the terminal, runways attempts to reduceflow variability, while the other attem...

Empirical Bayes Estimation in Nonstationary Markov chains

Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...