Most econometric analyses of patent data rely on regression methods using a parametric form of the predictor for modeling the dependence of the response given certain covariates. These methods often lack the capability of identifying non-linear relationships between dependent and independent...

Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM?s and extensions to generalized structured additive regression based on one or two dimensional P-splines...

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...

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 parametric modeling. Following these researchers, we suggest...

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...

This paper deals with the effects of entry into motherhood on women's employment dynamics. Our analysis is based on the complete lifetime working- and income histories of a 1% sample of all persons born between 1934 and 1971 and employed in West Germany sometime between 1975 and 1995. We use the...