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Double hierarchical generalized linear models (with discussion)
Authors:Youngjo Lee  John A Nelder
Institution:Seoul National University, Korea; Imperial College London, UK
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.
Keywords:Generalized linear models  Heavy-tailed distribution  Hierarchical generalized linear models  Hierarchical likelihood              h-likelihood  Joint generalized linear models  Random-effect models  Restricted maximum likelihood estimator  Stochastic volatility models
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