1 citations · 1 across the 2 of their papers we have counts for
8 papers
Sparse dimension reduction based on energy and ball statistics
Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck
As its name suggests, sufficient dimension reduction (SDR) targets to estimate a subspace from data that contains all information sufficient to explain a dependent variable. Ample…
direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods
Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck
The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction…
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Sebastiaan Höppner, Bart Baesens, Wouter Verbeke +1
Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection syste…
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Bart Baesens, Sebastiaan Höppner, Irene Ortner +1
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, frau…
Cellwise Robust M Regression
Peter Filzmoser, Sebastiaan Höppner, Irene Ortner +2
The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear mod…
Concordance probability in a big data setting: application in non-life insurance
Robin Van Oirbeek, Christopher Grumiau, Tim Verdonck
The concordance probability or C-index is a popular measure to capture the discriminatory ability of a regression model. In this article, the definition of this measure is adapted…