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Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Assessing the robustness of estimators when fitting Poisson inverse Gaussian models 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Metrika Springer Journals

Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Weems, Kimberly; Smith, Paul
Metrika , Volume 81 (8) – Jun 4, 2018
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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Statistics; Statistics, general; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods
ISSN
0026-1335
eISSN
1435-926X
DOI
10.1007/s00184-018-0664-1
Publisher site
See Article on Publisher Site

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.

Journal

Metrika – Springer Journals

Published: Jun 4, 2018

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