39 citations · 90 across the 18 of their papers we have counts for
19 papers
flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in R
Maximilian Willer, Peter Ruckdeschel
flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpose machine learning pipelines. I…
Minimal sample size in balanced ANOVA models of crossed, nested, and mixed classifications
Bernhard Spangl, Norbert Kaiblinger, Peter Ruckdeschel +1
We consider balanced one-, two- and three-way ANOVA models to test the hypothesis that the fixed factor A has no effect. The other factors are fixed or random. We determine the non…
The column measure and Gradient-Free Gradient Boosting
Tino Werner, Peter Ruckdeschel
Sparse model selection by structural risk minimization leads to a set of a few predictors, ideally a subset of the true predictors. This selection clearly depends on the underlying…
Generalized Pareto Processes and Liquidity
Sascha Desmettre, Johan de Kock, Peter Ruckdeschel +1
Motivated by the modeling of liquidity risk in fund management in a dynamic setting, we propose and investigate a class of time series models with generalized Pareto marginals: the…
Statistical models for dynamics in extreme value processes
Bernhard Spangl, Sascha Desmettre, Peter Ruckdeschel
We study four different approaches to model time-dependent extremal behavior: dynamics introduced by (a) a state-space model (SSM), (b) a shot-noise-type process with GPD marginals…
Robustification of Elliott's on-line EM algorithm for HMMs
Christina Erlwein, Peter Ruckdeschel
In this paper, we establish a robustification of an on-line algorithm for modelling asset prices within a hidden Markov model (HMM). In this HMM framework, parameters of the model…
Robust Kalman tracking and smoothing with propagating and non-propagating outliers
Peter Ruckdeschel, Bernhard Spangl, Daria Pupashenko
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 u…
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…
Infinitesimally Robust Estimation in General Smoothly Parametrized Models
Matthias Kohl, Peter Ruckdeschel, Helmut Rieder
We describe the shrinking neighborhood approach of Robust Statistics, which applies to general smoothly parametrized models, especially, exponential families. Equal generality is a…