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Double hierarchical generalized linear models (with discussion)

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  • Youngjo Lee
  • John A. Nelder

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.

Suggested Citation

  • Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:2:p:139-185
    DOI: 10.1111/j.1467-9876.2006.00538.x
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