models based on GAMLSS type approaches and quantile regression, respectively. Our models involve linear, nonlinear and … fields. Due to the high complexity of the models statistical inference is fully Bayesian and based on highly efficient Markov …

-term panel data from Germany, we build both mean and distributional regression models with the established 0.5 probability cutoff …

probabilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic … distributional models capturing location, scale, shape (and other aspects) of the response distribution. One of the most established … models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high …

. Inclusion of time-varying coefficients leads to models that relax the proportional hazards assumption. Nonlinear and time … for such general mixed hazard regression models is derived using penalized likelihood for regression coefficients and … application to leukemia survival data in Northwest England. -- extended Cox model ; structured hazard regression ; mixed models …

There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing … (MCMC) simulation techniques which allow the estimation of realistically complex models. This paper describes the … capabilities of the public domain software BayesX for estimating complex regression models with structured additive predictor. The …

4208 In many practical situations, simple regression models suffer from the fact that the dependence of responses on … may be present. While, in recent years, there has been a lot of work in this area dealing with univariate response models …, only limited attention has been given to models for multicategorical space-time data. We propose a general class of …