Default Bayesian model determination methods for generalised linear mixed models

Default Bayesian model determination methods for generalised linear mixed models

A default strategy for fully Bayesian model determination for GLMMs is considered which addresses the two key issues of default prior specification and computation. In particular, the concept of unit information priors is extended to the parameters of a GLMM. A combination of MCMC and Laplace approximations is used to compute approximations to the posterior model probabilities to find a subset ...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Bayesian Variable and Link Determination for Generalised Linear Models

Parsimonious classification via generalised linear mixed models

We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the result...

Bayesian inference for generalized linear mixed models.

Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample sizes especially, likelihood-based inference can be unreliable with variance components being particularly difficult to estimate. A Bayesian approach is appealing but has been hampered by the lack of a ...