39 citations · 90 across the 18 of their papers we have counts for
11 papers · 1 filter
Robust Estimation of Operational Risk
Nataliya Horbenko, Peter Ruckdeschel, Taehan Bae
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.…
General Purpose Convolution Algorithm in S4-Classes by means of FFT
Peter Ruckdeschel, Matthias Kohl
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…
Consequences of Higher Order Asymptotics for the MSE of M-estimators on Neighborhoods
Peter Ruckdeschel
In Ruckdeschel[10], we derive an asymptotic expansion of the maximal mean squared error (MSE) of location M-estimators on suitably thinned out, shrinking gross error neighborhoods.…
Higher order asymptotics for the MSE of the sample median on shrinking neighborhoods
Peter Ruckdeschel
We provide an asymptotic expansion of the maximal mean squared error (MSE) of the sample median to be attained on shrinking gross error neighborhoods about an ideal central distrib…
Higher Order Expansion for the MSE of M-estimators on shrinking neighborhoods
Peter Ruckdeschel
We consider estimation of a one-dimensional location parameter by means of M-estimators S_n with monotone influence curve psi. For growing sample size n, on suitably thinned out co…
Fisher Information in Group-Type Models
Peter Ruckdeschel
In proofs of L_2-differentiability, Lebesgue densities of a central distribution are often assumed right from the beginning. Generalizing Theorem 4.2 of Huber[81], we show that in…
Yet another breakdown point notion: EFSBP - illustrated at scale-shape models
Peter Ruckdeschel, Nataliya Horbenko
The breakdown point in its different variants is one of the central notions to quantify the global robustness of a procedure. We propose a simple supplementary variant which is use…
Robust Estimators in Generalized Pareto Models
Peter Ruckdeschel, Nataliya Horbenko
This paper deals with optimally-robust parameter estimation in generalized Pareto distributions (GPDs). These arise naturally in many situations where one is interested in the beha…
Fisher Information of Scale
Peter Ruckdeschel, Helmut Rieder
Motivated by the information bound for the asymptotic variance of M-estimates for scale, we define Fisher information of scale of any distribution function F on the real line as a…
Optimally Robust Kalman Filtering at Work: AO-, IO-, and Simultaneously IO- and AO- Robust Filters
Peter Ruckdeschel
We take up optimality results for robust Kalman filtering from Ruckdeschel[2001,2010] where robustness is understood in a distributional sense, i.e.; we enlarge the distribution as…
Optimally (Distributional-)Robust Kalman Filtering
Peter Ruckdeschel
We present optimality results for robust Kalman filtering where robustness is understood in a distributional sense, i.e.; we enlarge the distribution assumptions made in the ideal…