We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding...

This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing...

Several papers have recommended the Champernowne distribution to describe operational risklosses. This paper compares the tail performance of the Champernowne transformed kernel density estimator, the generalized Pareto distribution (gpd) and the g-and-h distribution. We introduce a new tail...

The subject of this paper is a one-method-fits-all approach to quantify and predict future losses in insurance. This method is based on a semiparametric estimator which is corrected by some nonparametric smoothing techniques. A number of alternative kernel functions are considered for removing...

We propose new procedures for estimating the univariate quantities of interest in both additive and multiplicative nonparametric marker dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring. Our procedures are based on kernels...

A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to...