Most recently proposed bandwidth selectors in kernel density estimation have been developed with intent to reduce the large sampling variability of Least Squares Cross-Validation. Their asymptotic superiority has been shown in many papers. Some of those selectors have even the fastest n-1/ 2...

In this paper we investigate the gains of using nonparametric estimation methods in a family of models related to Generalised Linear Models. We focus especially on discrete choice models. We give an overview on different nonparametric and semiparametric approaches in this setting. In particular...

We show that both parametric distribution functions appearing in extreme value theory have log-concave densities if the extreme value index [gamma][set membership, variant][-1,0] and that all distribution functions F with log-concave density belong to the max-domain of attraction of the...

In this paper we present the weighted least squares estimator for the extreme value index, and prove its consistency and asymptotic normality.

Many popular methods of model selection involve minimizing a penalized function of the data (such as the maximized log-likelihood or the residual sum of squares) over a set of models. The penalty in the criterion function is controlled by a penalty multiplier λ which determines the properties...