Semiparametric Stepwise Regression to Estimate Sales Promotion Effects

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 another semiparametric framework which is based on penalized B-splines to analyze sales promotion effects flexibly. Unlike these researchers, we introduce a stepwise procedure with simultaneous smoothing parameter choice for variable selection. Applying this stepwise routine enables us to deal with product categories with many competitive items without imposing restrictions on the competitive market structure in advance. We illustrate the new methodology in an empirical application using weekly store-level scanner data

Semiparametric Stepwise Regression to Estimate Sales Promotion Effects

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 another semiparametric framework which is based on penalized B-splines to analyze sales promotion effects flexibly. Unlike these researchers, we introduce a stepwise procedure with simultaneous smoothing parameter choice for variable selection. Applying this stepwise routine enables us to deal with product categories with many competitive items without imposing restrictions on the competitive market structure in advance. We illustrate the new methodology in an empirical application using weekly store-level scanner data

Model selection in generalised structured additive regression models

In recent years data sets have become increasingly more complex requiring more flexible instruments for their analysis. Such a flexible instrument is regression analysis based on a structured additive predictor which allows an appropriate modelling for different types of information, e.g.~by using smooth functions for spatial information, nonlinear functions for continuous covariates or by using effects for the modelling of cluster--specific heterogeneity. In this thesis, we review many important effects. Moreover, we place an emphasis on interaction terms and introduce a possibility for the simple modelling of a complex interaction between two continuous covariates. \\ Mainly, this thesis is concerned with the topic of variable and smoothing parameter selection within structured additive regression models. For this purpose, we introduce an efficient algorithm that simultaneously selects relevant covariates and the degree of smoothness for their effects. This algorithm is even capable of handling complex situations with many covariates and observations. Thereby, the validation of different models is based on goodness of fit criteria, like e.g.~AIC, BIC or GCV. The methodological development was strongly motivated by case studies from different areas. As examples, we analyse two different data sets regarding determinants of undernutrition in India and of rate making for insurance companies. Furthermore, we examine the performance or our selection approach in several extensive simulation studies