Bayesian Subset Selection for Reproductive Dispersion Linear Models

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 Selection of Log-Linear Models

A general methodology is presented for nding suitable Poisson log-linear models with applications to multiway contingency tables. Mixtures of multivariate normal distributions are used to model prior opinion when a subset of the regression vector is believed to be nonzero. This prior distribution is studied for two and three-way contingency tables, in which the regression coe cients are interpr...

Adaptive Bayesian Criteria in Variable Selection for Generalized Linear Models

For the problem of variable selection in generalized linear models, we develop various adaptive Bayesian criteria. Using a hierarchical mixture setup for model uncertainty, combined with an integrated Laplace approximation, we derive Empirical Bayes and Fully Bayes criteria that can be computed easily and quickly. The performance of these criteria is assessed via simulation and compared to othe...

Group subset selection for linear regression

Two fast group subset selection (GSS) algorithms for the linear regression model are proposed in this paper. GSS finds the best combinations of groups up to a specified size minimising the residual sum of squares. This imposes an l0 constraint on the regression coefficients in a group context. It is a combinatorial optimisation problem with NP complexity. To make the exhaustive search very effi...

Bayesian variable selection in linear mixed effects models