Estimation for mixtures of Markov processes
Finite mixtures of Markov processes with densities belonging to exponential families are introduced. Quasi-likelihood and maximum likelihood methods are used to estimate the parameters of the mixing distributions and of the component distributions. The E-M algorithm is used to compute the ML estimates. Mixture of Autoregressive processes and of two-state Markov chains are discussed as specific examples. Simulation results on the comparison of quasi-likelihood and ML estimates are reported.
Volume (Year): 59 (2002)
Issue (Month): 3 (October)
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- Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 1: Methodology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 855-869.
- Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
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