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Estimation for mixtures of Markov processes

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  • Park, Jeong-gun
  • Basawa, I. V.

Abstract

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.

Suggested Citation

  • Park, Jeong-gun & Basawa, I. V., 2002. "Estimation for mixtures of Markov processes," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 235-244, October.
    • Handle: RePEc:eee:stapro:v:59:y:2002:i:3:p:235-244
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Peiming Wang & Iain Cockburn & Martin L. Puterman, "undated". "A Mixed Poisson Regression Model for Analysis of Patent Data," Computing in Economics and Finance 1996 _049, Society for Computational Economics.
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