P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparametric regression models. Recently, a Bayesian version for P-splines has been developed on the basis of Markov chain Monte Carlo simulation techniques for inference. In this work we adopt and...

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...

Designing and pricing new products is one of the most critical activities for a firm, and it is well-known that taking into account consumer preferences for design decisions is essential for products later to be successful in a competitive environment (e.g., Urban and Hauser 1993). Consequently,...

SUMMARY Semiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods. Using retail store‐level data, we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy. We find that (a)...

Die Marktsegmentierung zählt neben der Neuproduktplanung und Preisgestaltung zu den wesentlichen Einsatzgebieten der Conjoint-Analyse. Neben traditionell eingesetzten zweistufigen Vorgehensweisen, bei denen Conjoint-Analyse und Segmentierung in zwei getrennten Schritten erfolgen, stehen heute...

We discuss inference for additive models with random scaling factors. The additive effects are of the form (1+g)f(z) where f is a nonlinear function of the continuous covariate z modeled by P(enalized)-splines and 1+g is a random scaling factor. Additionally, monotonicity constraints on the...