A shrinkage type estimator is introduced which has favorable properties in binary regression. Although binary observations are never very far away from the underlying probability, in all interesting cases there is a non-zero distance between observation and underlying mean. The proposed response...

In many applications it is known that the underlying smooth function is constrained to have a specific form. In the present paper, we propose an estimation method based on the regression spline approach, which allows to include concavity or convexity constraints in an appealing way. Instead of...

A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors. As the elastic net, the method encourages a grouping effect where strongly correlated...

In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The...

Gene expression datasets usually have thousends of explanatory variables which are observed on only few samples. Generally most variables of a dataset have no effect and one is interested in eliminating these irrelevant variables. In order to obtain a subset of relevant variables an appropriate...

In this paper R2-type measures of the explanatory power of multivariate linear and categorical probit models proposed in the literature are reviewed and their deficiencies are discussed. It is argued that a measure of the explanatory power should take into account the components which are...