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On Dynamic Generalized Linear Models with Applications

Author

Listed:
  • Sourish Das

    (SAS Research and Development)

  • Dipak K. Dey

    (University of Connecticut)

Abstract

In this paper we combine the idea of ‘power steady model’, ‘discount factor’ and ‘power prior’, for a general class of filter model, more specifically within a class of dynamic generalized linear models (DGLM). We show an optimality property for our proposed method and present the particle filter algorithm for DGLM as an alternative to Markov chain Monte Carlo method. We also present two applications; one on dynamic Poisson models for hurricane count data in Atlantic ocean and the another on the dynamic Poisson regression model for longitudinal count data.

Suggested Citation

  • Sourish Das & Dipak K. Dey, 2013. "On Dynamic Generalized Linear Models with Applications," Methodology and Computing in Applied Probability, Springer, vol. 15(2), pages 407-421, June.
    • Handle: RePEc:spr:metcap:v:15:y:2013:i:2:d:10.1007_s11009-011-9255-6
    DOI: 10.1007/s11009-011-9255-6
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    References listed on IDEAS

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    1. Ibrahim J.G. & Chen M-H. & Sinha D., 2003. "On Optimality Properties of the Power Prior," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 204-213, January.
    2. Michael H. Rothkopf, 2007. "Decision Analysis: The Right Tool for Auctions," Decision Analysis, INFORMS, vol. 4(3), pages 167-172, September.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Zellner, Arnold, 2002. "Information processing and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 41-50, March.
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    Citations

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    Cited by:

    1. Rajiv Sambasivan & Sourish Das & Sujit K. Sahu, 2020. "A Bayesian perspective of statistical machine learning for big data," Computational Statistics, Springer, vol. 35(3), pages 893-930, September.
    2. Parfait Munezero, 2022. "Efficient particle smoothing for Bayesian inference in dynamic survival models," Computational Statistics, Springer, vol. 37(2), pages 975-994, April.
    3. Sourish Das, 2018. "Modeling Nelson-Siegel Yield Curve using Bayesian Approach," Papers 1809.06077, arXiv.org, revised Oct 2018.

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