236 citations · 344 across the 8 of their papers we have counts for
8 papers
Efficient Computation of Sparse and Robust Maximum Association Estimators
Pia Pfeiffer, Andreas Alfons, Peter Filzmoser
Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization tec…
When Respondents Don't Care Anymore: Identifying the Onset of Careless Responding
Max Welz, Andreas Alfons
Questionnaires in the behavioral sciences tend to be lengthy. However, literature suggests that survey length is a contributing factor to careless responding, with longer questionn…
Tandem clustering with invariant coordinate selection
Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen +1
For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach us…
Robust Mediation Analysis: The R Package robmed
Andreas Alfons, Nüfer Y. Ateş, Patrick J. F. Groenen
Mediation analysis is one of the most widely used statistical techniques in the social, behavioral, and medical sciences. Mediation models allow to study how an independent variabl…
SparseStep: Approximating the Counting Norm for Sparse Regularization
Gerrit J. J. van den Burg, Patrick J. F. Groenen, Andreas Alfons
The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem. The algorithm works by adding an approximation of the exact…
The shooting S-estimator for robust regression
Viktoria Öllerer, Andreas Alfons, Christophe Croux
To perform multiple regression, the least squares estimator is commonly used. However, this estimator is not robust to outliers. Therefore, robust methods such as S-estimation have…
The Influence Function of Penalized Regression Estimators
Viktoria Öllerer, Christophe Croux, Andreas Alfons
To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers. Therefor…
Sparse least trimmed squares regression for analyzing high-dimensional large data sets
Andreas Alfons, Christophe Croux, Sarah Gelper
Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common probl…