Bayesian Effect Selection in Structured Additive Distributional Regression Models

Bayesian structured additive distributional regression

In this paper, we propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, int...

BayesX: Analysing Bayesian structured additive regression models

SUMMARY There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This paper describes the capabilities of the pu...

Random intercept selection in structured additive regression models

Bayesian inference for structured additive quantile regression models

Most quantile regression problems in practice require flexible semiparametric forms of the predictor for modeling the dependence of responses on covariates. Furthermore, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal data. We present a unified approach for Bayesian quantile inference via Markov chai...

Bayesian regularization and model choice in structured additive regression

In regression models with a large number of potential model terms, the selection of an appropriate subset of covariates and their interactions is an important challenge for data analysis, as is the choice of the appropriate representation of their impact on the quantities to be estimated such as deciding between linear or smooth non-linear effects. The main part of this work is dedicated to the...