by Peter Ruckdeschel, Matthias Kohl, Thomas Stabla, and Florian Camphausen distr is an
R package that provides a conceptual treatment of random variables (rv’s) by means of S4–…

According to the Loss Distribution Approach, the operational risk of a bank is determined as
99.9% quantile of the respective loss distribution, covering unexpected severe events. The …

A common situation in filtering where classical Kalman filtering does not perform particularly
well is tracking in the presence of propagating outliers. This calls for robustness understood …

We present some optimality results for robust Kalman filtering. To this end, we introduce the
general setup of state space models which will not be limited to a Euclidean or time-discrete …

Object orientation provides a flexible framework for the implementation of the convolution of
arbitrary distributions of real-valued random variables. We discuss an algorithm which is …

Robust Statistics considers the quality of statistical decisions in the presence of deviations
from the ideal model, where deviations are modelled by neighborhoods of a certain size about …

Based on a robustness concept adapted from mathematical statistics, we investigate robust
optimal investment strategies for worst-case crash scenarios when the maximum crash …

We introduce a refined tree method to compute option prices using the stochastic volatility
model of Heston. In a first step, we model the stock and variance process as two separate …

We study global and local robustness properties of several estimators for shape and scale
in a generalized Pareto model. The estimators considered in this paper cover maximum …

In this paper we give a motivation for the shrinking rate $$1/ \sqrt{n}:$$ let p 0 and q n be the
outlier probability under the ideal model, and some member of a neighborhood about this …