Markov Poisson regression models for discrete time series. Part 1: Methodology

Peiming Wang and Martin Puterman

Journal of Applied Statistics, 1999, vol. 26, issue 7, 855-869

Abstract: This paper proposes and investigates a class of Markov Poisson regression models in which Poisson rate functions of covariates are conditional on unobserved states which follow a finite-state Markov chain. Features of the proposed model, estimation, inference, bootstrap confidence intervals, model selection and other implementation issues are discussed. Monte Carlo studies suggest that the proposed estimation method is accurate and reliable for single- and multiple-subject time series data; the choice of starting probabilities for the Markov process has little eff ect on the parameter estimates; and penalized likelihood criteria are reliable for determining the number of states. Part 2 provides applications of the proposed model.

Date: 1999
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DOI: 10.1080/02664769922098

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