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A New Strategy for Choosing Initial for the EM Algorithm in poisson regression models

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2013年第1期31卷 1-8页

作者： Faria, Susana Goncalves, Fatima Univ Minho Math Res Ctr Dept Math & Applicat P-4800058 Guimaraes Portugal Univ Minho P-4800058 Guimaraes Portugal

The EM algorithm is the standard tool for maximum likelihood estimation in finite mixture models. Its most important drawbacks are the slow convergence, the need for a suitable stopping criterion and the choice of the initial values. In this paper, we focus on the issue of selecting initial values for the EM algorithm in mixture poisson regression models. A new strategy, aiming at overcoming limitations of other approaches, is proposed and a simulation study comparing its performance with two alternative strategies is carried out. In models with overlapped components and/or not similar mixing proportions, the new strategy has proven to provide more accurate parameter estimates and to require a fewer number of iterations until the EM algorithm convergence saving computing time.

关键词： EM algorithm mixture poisson regression models Count Data Simulation Study

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Comparison of EM and SEM Algorithms in poisson regression models: A Simulation Study

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2012年第4期41卷 497-509页

作者： Faria, Susana Soromenho, Gilda Univ Minho Dept Math & Applicat P-4800058 Guimaraes Portugal Univ Lisbon Inst Educ P-1699 Lisbon Portugal

In this work, we propose to compare two algorithms to compute maximum likelihood estimates for the parameters of a mixture poisson regression model: the EM algorithm and the Stochastic EM algorithm. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets and real data sets. Simulation results show that the choice of the approach depends essentially on the overlap of the regression lines. In the real data case, we show that the Stochastic EM algorithm resulted in model estimates that best fit the regression model.

关键词： EM algorithm Maximum likelihood estimation mixture poisson regression models Simulation study Stochastic EM algorithm

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