identify significant district- specific scaling factors. According to the deviance information criterion, the models … incorporating these factors perform significantly better than standard models with- out random scaling factors. …

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 …

Kalyanam and Shively (1998) and van Heerde et al. (2001) have proposed semiparametric models to estimate the influence … of price promotions on brand sales, and both obtained superior performance for their models compared to strictly …

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 …

regression models with binary responses. The approach extends previous work by Lang et al. (2002) for Gaussian responses … parameters. Estimation is fully Bayesian and uses latent utility representations of binary regression models for efficient block …