9 papers
Outlier detection in state-space models using mean-shift penalisation
Rajan Shankar, Ines Wilms, Jakob Raymaekers +1
State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We…
Cellwise Outliers
Mia Hubert, Jakob Raymaekers, Peter J. Rousseeuw
In statistics and machine learning, the traditional meaning of the terms `outlier' and `anomaly' is a case in the dataset that behaves differently from the bulk of the data, which…
Least trimmed squares regression with missing values and cellwise outliers
Jakob Raymaekers, Peter J. Rousseeuw
Regression is the workhorse of statistics, and is often faced with real data that contain outliers. When these are casewise outliers, that is, cases that are entirely wrong or belo…
Robust Distance Covariance
Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw
Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual d…
Independent Component Analysis by Robust Distance Correlation
Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw +2
Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunat…
A Powerful Random Forest Featuring Linear Extensions (RaFFLE)
Jakob Raymaekers, Peter J. Rousseeuw, Thomas Servotte +2
Random forests are widely used in regression. However, the decision trees used as base learners are poor approximators of linear relationships. To address this limitation we propos…