39 citations · 90 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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 (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…