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Original Articles

Comparison of EM and SEM Algorithms in Poisson Regression Models: A Simulation Study

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Pages 497-509 | Received 08 Feb 2010, Accepted 31 May 2011, Published online: 20 Dec 2011

References

  • Aitkin , M. ( 1996 ). A general maximum likelihood analysis of overdispersion in generalized linear models . Statistics and Computing 6 : 251 – 262 .
  • Celeux , G. , Diebolt , J. ( 1985 ). The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem . Computational Statistics Quarterly 2 : 73 – 82 .
  • Celeux , G. , Govaert , G. ( 1993 ). Comparison of the mixture and the classification maximum likelihood in cluster analysis . Journal of Statistical Computation and Simulation 47 : 127 – 146 .
  • Celeux , G. , Chauveau , D. , Diebolt , J. ( 1996 ). Stochastic versions of the EM algorithm: an experimental study in the mixture case . Journal of Statistical Computation and Simulation 55 : 287 – 314 .
  • Dempster , A. P. , Laird , N. M. , Rubin , D. B. ( 1977 ). Maximum likelihood from incomplete data via the EM algorithm . Journal of the Royal Statistical Society B 39 : 1 – 38 .
  • Dias , J. , Wedel , M. ( 2004 ). An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods . Statistics and Computing 14 : 323 – 332 .
  • Diebolt , J. , Ip , E. H. S. ( 1996 ). Stochastic EM: method and application . In: Gilks , W. R. , Richardson , S. , Speigelhalter , D. J. , eds. Markov Chain Monte Carlo in Practice . London : Chapman & Hall .
  • Faria , S. , Soromenho , G. ( 2010 ). Fitting mixtures of linear regressions . Journal of Statistical Computation and Simulation 80 ( 2 ): 201 – 225 .
  • Fruhwirth-Schnatter , S. ( 2006 ). Finite Mixture and Markov Switching Models . Heidelberg : Springer .
  • Hastie , T. , Tibshirani , R. , Friedman , J. ( 2001 ). Elements of Statistical Learning . New York : Springer .
  • Leisch , F. ( 2004 ). FlexMix: A general framework for finite mixture models and latent class regression in R . Journal of Statistical Software 11 ( 8 ): 1 – 18 .
  • McLachlan , G. J. , Peel , D. ( 2000 ). Finite Mixture Models . New York : Wiley .
  • R Development Core Team . ( 2008 ). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria .
  • Teicher , H. ( 1960 ). On the mixture of distributions . Annals of Mathematical Statistics 31 : 55 – 73 .
  • Wang , P. , Puterman , M. L. , Cockburn , I. M. , Le , N. ( 1996 ). Mixed Poisson regression models with covariate dependent rates . Biometrics 52 ( 2 ): 381 – 400 .
  • Wedel , M. , Desarbo , W. S. , Bult , J. R. , Ramaswamy , V. ( 1993 ). A latent class Poisson regression model for heterogeneous count data . Journal of Applied Econometrics 8 ( 4 ): 397 – 411 .
  • Yang , M. S. , Lai , C. Y. ( 2005 ). Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis . Soft Computing 9 : 519 – 524 .

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