Skip to main content
×
×
Home

EM Algorithm for Mixed Poisson and Other Discrete Distributions

  • Dimitris Karlis (a1)
Abstract

Mixed Poisson distributions are widely used in various disciplines including actuarial applications. The family of mixed Poisson distributions contains several members according to the choice of the mixing distribution for the parameter of the Poisson distribution. Very few of them have been studied in depth, mainly because of algebraic intractability. In this paper we will describe an EM type algorithm for maximum likelihood estimation for mixed Poisson distributions. The main achievement is that it reduces the problem of estimation to one of estimation of the mixing distribution which is usually easier. Variants of the algorithm work even when the probability function of the mixed distribution is not known explicitly but we have only an approximation of it. Other discrete distributions are treated as well.

    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      EM Algorithm for Mixed Poisson and Other Discrete Distributions
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      EM Algorithm for Mixed Poisson and Other Discrete Distributions
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      EM Algorithm for Mixed Poisson and Other Discrete Distributions
      Available formats
      ×
Copyright
References
Hide All
Aitkin, M. (1996) A General Maximum Likelihood Analysis of Overdispersion in Generalized Linear Models. Statistics and Computing, 6, 251262.
Booth, J.G. and Caffo, B.S. (2002) Unequal sampling for Monte Carlo EM algorithms. Computational Statistics and Data Analaysis, 39, 261270.
Booth, J.G. and Hobert, J.P. (1999) Maximizing generalized linear mixed model likelihood with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B, 61, 265285.
Bohning, D. (1999) Computer assisted analysis of mixtures and applications in meta-analysis, disease mapping and others. CRC Press, New York.
Bulmer, M. (1974) On fitting the Poisson-lognormal distribution to species-abundance data. Bio-metrics, 30, 101110.
Carlin, B.P., and Louis, T.A. (1996) Bayes and Empirical Bayes Methods for Data Analysis. Great Britain: Chapman and Hall.
Celeux, G. and Diebolt, J. (1985) The SEM Algorithm, a Probabilistic Teacher Algorithm Derived from the EM Algorithm for the Mixture Problem. Computational Statistics Quarterly, 2, 7382.
Chan, K.S. and Ledolter, J. (1995) Monte Carlo EM estimation for time series models involving counts. Journal of the American Statistical Association, 90, 242252.
Cowles, M.K. and Carlin, B.P. (1996) Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Journal of the American Statistical Association, 91, 883904.
Dempster, A.P., Laird, N.M. and Rubin, D. (1977) Maximum Likelihood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society, B 39, 138.
Douglas, J.B. (1980) Analysis With Standard Contagious Distributions. Statistical distributions in scientific work series, Vol 4, ICPH, Maryland, USA.
Douglas, J.B. (1994) Empirical Fitting of Discrete Distributions. Biometrics, 50, 576579.
Goutis, C. (1993) Recovering Extra Binomial Variation. Journal of Statistical Computation and Simulation, 45, 233242.
Grandell, J. (1997) Mixed Poisson Processes. Chapman and Hall/CRC Statistics and Mathematics, New York.
Griffiths, D.A. (1973) Maximum Likelihood Estimation for the Beta Binomial Distributions and an Application to the Household Distribution of the Total Number of Cases of a Disease. Biometrics, 29, 637648.
Greenwood, M. and Yule, G. (1920) An Inquiry Into the Nature of Frequency Distributions Representative of Multiple Happenings with Particular Reference to the Occurence of Multiple Attacks of Disease Or of Repeated Accidents. Journal of the Royal Statistical Society, A 83, 255279.
Johnson, N., Kotz, S. and Kemp, A.W. (1992) Univariate discrete distributions, Willey-New York.
Jorgensen, B. (1982) The generalized Inverse Gaussian distribution. Lecture Notes in Statistics, 9, Springer-Verlag.
Karlis, D. (1998) Estimation and hypothesis testing problems in Poisson mixtures. Phd Thesis, Department of Statistics, Athens University of Economics.
Karlis, D. (2001) Exact ML estimation for Mixed Poisson Regression Models. Statistical Modelling: An International Journal, 1, 305319.
Kemp, C.D. and Kemp, A.W. (1965) Some Properties of the Hermite Distribution. Biometrika, 52, 381394.
Kemp, A.W. and Kemp, C.D. (1966) An Alternative Derivation of the Hermite Distribution, Biometrika, 53, 627628.
Levine, R.A. and Casella, G. (2001) Implementations of the Monte Carlo EM Algorithm. Journal of Computational and Graphical Statistics, 10, 422439.
Lindsay, B. (1995) Mixture Models: Theory, Geometry and Applications. Regional Conference Series in Probability and Statistics, Vol 5, Institute of Mathematical Statistics and American Statistical Association.
McCulloch, P. (1997) Maximum Likelihood algorithms for generalized linear mixed models. Journal of the American statistical Association, 92, 162170.
McLachlan, G. and Peel, D. (2000) Finite mixture models, Wiley, New York.
Meng, X.L. and Rubin, D. (1993) Maximum Likelihood Estimation via the ECM Algorithm: A General Framework. Biometrika, 80, 267278.
Munkin, M.K. and Trivedi, P.K. (1999) Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models, with Application. Econometrics Journal, 2, 2948.
Ong, S.H. (1995) Computation of Probabilities of a Generalised log-series and Related Distributions. Communication in Statistics – Theory and Methods, 24, 253271.
Panjer, H. (1981) Recursive Evaluation of a Family of Compound Distributions. ASTIN Bulletin, 18, 5768.
Piegorsch, W.W. (1990) Maximum Likelihood Estimation for the Negative Binomial Dispersion Paramater. Biometrics, 46, 863867.
Sankaran, M. (1970) The Discrete Poisson-Lindley Distribution. Biometrics, 26, 145149.
Sapatinas, T. (1995) Identifiability of mixtures of power-series distributions and related characterizations, Annals of the Institute of Statistical Mathematics, 47, 447459.
Sichel, H.S. (1974) On a distribution representing sentence-length in written prose. Journal of the Royal Statistical Society, A 137, 2534.
Sichel, H.S. (1982) Asymptotic efficiencies of three methods of estimation for the inverse gaussian-Poisson distribution. Biometrika, 69, 467472.
Shaban, S.A. (1988) Poisson-Lognormal Distributions. In Crow, E.L. and Shimizu, K. (eds), Log-normal Distributions: Theory and Applications, 195210, New York, Marcel and Dekker.
Sprott, D. (1983) Estimating the parameters of a convolution by the Maximum Likelihood. Journal of the American Statistical Association, 78, 457460.
Stewart, J.A. (1994) The Poisson-Lognormal Model for Bibliometric/Scientometric Distributions. Information Processing and Management, 30, 239251.
Tanner, M.A. (1996) Tools for statistical inference: Methods for the Exploration of Posterior distributions and Likelihood Functions, 3rd edition, Springer, New York.
Titterington, D.M, Smith, A.F.M. and Makov, U.E. (1985) Statistical analysis of finite mixtures distributions. Willey and sons, New York.
Tripathi, R., Gupta, R. and Gurland, J. (1994) Estimation of the Parameters in the Beta Binomial Model. Annals of the Institute of Statistical Mathematics, 46, 317331.
Wang, Y. (1996) Estimation Problems for the Two-Parameter Negative Binomial Distribution. Statistics and Probability Letters, 26, 113114.
Wei, G.C.G. and Tanner, M.A. (1990) A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association, 85, 699704.
Willmot, G. (1993) On Recursive Evaluation of Mixed Poisson Probabilities and Related Quantities. Scandinavian Actuarial Journal, 101113.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

ASTIN Bulletin: The Journal of the IAA
  • ISSN: 0515-0361
  • EISSN: 1783-1350
  • URL: /core/journals/astin-bulletin-journal-of-the-iaa
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 1
Total number of PDF views: 271 *
Loading metrics...

Abstract views

Total abstract views: 405 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 13th June 2018. This data will be updated every 24 hours.