14 citations · 18 across the 9 of their papers we have counts for
5 papers · 1 filter
Robust Statistics meets elicitability: When fair model validation breaks down
Tino Werner
A crucial part of data analysis is the validation of the resulting estimators, in particular, if several competing estimators need to be compared. Whether an estimator can be objec…
Trimming Stability Selection increases variable selection robustness
Tino Werner
Contamination can severely distort an estimator unless the estimation procedure is suitably robust. This is a well-known issue and has been addressed in Robust Statistics, however,…
Quantitative robustness of instance ranking problems
Tino Werner
Instance ranking problems intend to recover the true ordering of the instances in a data set with a variety of applications in for example scientific, social and financial contexts…
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…
Asymptotic linear expansion of regularized M-estimators
Tino Werner
Parametric high-dimensional regression analysis requires the usage of regularization terms to get interpretable models. The respective estimators can be regarded as regularized M-f…