Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Assessing the robustness of estimators when fitting Poisson inverse Gaussian models Metrika https://doi.org/10.1007/s00184-018-0664-1 Assessing the robustness of estimators when fitting Poisson inverse Gaussian models 1 2 Kimberly S. Weems · Paul J. Smith Received: 24 October 2017 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 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. Keywords Poisson mixed models · Inverse Gaussian distribution · Influence function · Directional derivative · Maximum likelihood estimation 1 Introduction Poisson mixed models, a class of generalized linear mixed models (McCulloch et al. 2008), are often used to analyze count data that exhibit overdispersion. For example, see Dean and Nielsen (2007), Karlis and Xekalaki (2005), Ven and Weber (1995), and Hougaard et al. (1997). For these models, we assume that the conditional distri- bution of the response follows a Poisson distribution with a random mean. The mean incorporates the random effects used to model the overdispersion. This work was supported http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Metrika Springer Journals

Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Metrika , Volume OnlineFirst – Jun 4, 2018
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Publisher
Springer Berlin Heidelberg
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
D.O.I.
10.1007/s00184-018-0664-1
Publisher site
See Article on Publisher Site

Abstract

Metrika https://doi.org/10.1007/s00184-018-0664-1 Assessing the robustness of estimators when fitting Poisson inverse Gaussian models 1 2 Kimberly S. Weems · Paul J. Smith Received: 24 October 2017 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 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. Keywords Poisson mixed models · Inverse Gaussian distribution · Influence function · Directional derivative · Maximum likelihood estimation 1 Introduction Poisson mixed models, a class of generalized linear mixed models (McCulloch et al. 2008), are often used to analyze count data that exhibit overdispersion. For example, see Dean and Nielsen (2007), Karlis and Xekalaki (2005), Ven and Weber (1995), and Hougaard et al. (1997). For these models, we assume that the conditional distri- bution of the response follows a Poisson distribution with a random mean. The mean incorporates the random effects used to model the overdispersion. This work was supported

Journal

MetrikaSpringer Journals

Published: Jun 4, 2018

References

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