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Double hierarchical generalized linear models (with discussion)
Author(s) -
Lee Youngjo,
Nelder John A.
Publication year - 2006
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2006.00538.x
Subject(s) - heteroscedasticity , outlier , class (philosophy) , dispersion (optics) , random effects model , mathematics , hierarchical database model , computer science , linear model , statistical physics , algorithm , statistics , artificial intelligence , data mining , physics , medicine , meta analysis , optics
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.