Estimation for mixtures of Markov processes
AbstractFinite 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 59 (2002)
Issue (Month): 3 (October)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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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