Double hierarchical generalized linear models (with discussion)

Double hierarchical generalized linear models (with discussion) Summary.  We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy‐tailed distributions to be explored, providing robust estimation against outliers. The h‐likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society: Series C (Applied Statistics) Wiley

Double hierarchical generalized linear models (with discussion)

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Publisher
Wiley
Copyright
Copyright © 2006 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0035-9254
eISSN
1467-9876
DOI
10.1111/j.1467-9876.2006.00538.x
Publisher site
See Article on Publisher Site

Abstract

Summary.  We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy‐tailed distributions to be explored, providing robust estimation against outliers. The h‐likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities.

Journal

Journal of the Royal Statistical Society: Series C (Applied Statistics)Wiley

Published: Apr 1, 2006

Keywords: ; ; ; ; ; ; ; ;

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