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Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Author

Listed:
  • Kimberly S. Weems

    (North Carolina Central University)

  • Paul J. Smith

    (University of Maryland)

Abstract

The generalized linear mixed model (GLMM) extends classical regression analysis to non-normal, correlated response data. Because inference for GLMMs can be computationally difficult, simplifying distributional assumptions are often made. We focus on the robustness of estimators when a main component of the model, the random effects distribution, is misspecified. Results for the maximum likelihood estimators of the Poisson inverse Gaussian model are presented.

Suggested Citation

  • Kimberly S. Weems & Paul J. Smith, 2018. "Assessing the robustness of estimators when fitting Poisson inverse Gaussian models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 985-1004, November.
    • Handle: RePEc:spr:metrik:v:81:y:2018:i:8:d:10.1007_s00184-018-0664-1
    DOI: 10.1007/s00184-018-0664-1
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    References listed on IDEAS

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    3. Weems, K. S. & Smith, P. J., 2004. "On robustness of maximum likelihood estimates for Poisson-lognormal models," Statistics & Probability Letters, Elsevier, vol. 66(2), pages 189-196, January.
    4. Karlis, Dimitris, 2005. "EM Algorithm for Mixed Poisson and Other Discrete Distributions," ASTIN Bulletin, Cambridge University Press, vol. 35(1), pages 3-24, May.
    5. Teugels, J. L. & Willmot, G., 1987. "Approximations for stop-loss premiums," Insurance: Mathematics and Economics, Elsevier, vol. 6(3), pages 195-202, July.
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