Double hierarchical generalized linear models

In my talk I will introduce a class of double hierarchical generalized linear models (DHGLMs) in which random effects can be specified for both 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. If time allowed I will talk about the h-likelihood inferences related with missing data problems, wavelet smoothings and robust modellings.