60 citations · 361 across the 33 of their papers we have counts for
33 papers
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…
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…
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…
RobPy: a Python Package for Robust Statistical Methods
Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw +2
Robust estimation provides essential tools for analyzing data that contain outliers, ensuring that statistical models remain reliable even in the presence of some anomalous data. W…
Robust discriminant analysis
Mia Hubert, Jakob Raymaekers, Peter J. Rousseeuw
Discriminant analysis (DA) is one of the most popular methods for classification due to its conceptual simplicity, low computational cost, and often solid performance. In its stand…
Distance Covariance, Independence, and Pairwise Differences
Jakob Raymaekers, Peter J. Rousseeuw
(To appear in The American Statistician.) Distance covariance (Székely, Rizzo, and Bakirov, 2007) is a fascinating recent notion, which is popular as a test for dependence of any t…
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…
TSLiNGAM: DirectLiNGAM under heavy tails
Sarah Leyder, Jakob Raymaekers, Tim Verdonck
One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependenc…
Generalized Spherical Principal Component Analysis
Sarah Leyder, Jakob Raymaekers, Tim Verdonck
Outliers contaminating data sets are a challenge to statistical estimators. Even a small fraction of outlying observations can heavily influence most classical statistical methods.…
Fast Linear Model Trees by PILOT
Jakob Raymaekers, Peter J. Rousseeuw, Tim Verdonck +1
Linear model trees are regression trees that incorporate linear models in the leaf nodes. This preserves the intuitive interpretation of decision trees and at the same time enables…
Challenges of cellwise outliers
Jakob Raymaekers, Peter J. Rousseeuw
It is well-known that real data often contain outliers. The term outlier typically refers to a case, that is, a row of the data matrix. In recent times a different typ…
The Influence Function of Graphical Lasso Estimators
Gaëtan Louvet, Jakob Raymaekers, Germain Van Bever +1
The precision matrix that encodes conditional linear dependency relations among a set of variables forms an important object of interest in multivariate analysis. Sparse estimation…
The Cellwise Minimum Covariance Determinant Estimator
Jakob Raymaekers, Peter J. Rousseeuw
The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave…
Equivariant Passing-Bablok regression in quasilinear time
Jakob Raymaekers, Florian Dufey
Passing-Bablok regression is a standard tool for method and assay comparison studies thanks to its place in industry guidelines such as CLSI. Unfortunately, its computational cost…
Silhouettes and quasi residual plots for neural nets and tree-based classifiers
Jakob Raymaekers, Peter J. Rousseeuw
Classification by neural nets and by tree-based methods are powerful tools of machine learning. There exist interesting visualizations of the inner workings of these and other clas…
Weight-of-evidence 2.0 with shrinkage and spline-binning
Jakob Raymaekers, Wouter Verbeke, Tim Verdonck
In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of class…
Regularized K-means through hard-thresholding
Jakob Raymaekers, Ruben H. Zamar
We study a framework of regularized -means methods based on direct penalization of the size of the cluster centers. Different penalization strategies are considered and compared…
Real-time discriminant analysis in the presence of label and measurement noise
Iwein Vranckx, Jakob Raymaekers, Bart De Ketelaere +2
Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center a…
Class maps for visualizing classification results
Jakob Raymaekers, Peter J. Rousseeuw, Mia Hubert
Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of a…
Transforming variables to central normality
Jakob Raymaekers, Peter J. Rousseeuw
Many real data sets contain numerical features (variables) whose distribution is far from normal (gaussian). Instead, their distribution is often skewed. In order to handle such da…
Handling cellwise outliers by sparse regression and robust covariance
Jakob Raymaekers, Peter J. Rousseeuw
We propose a data-analytic method for detecting cellwise outliers. Given a robust covariance matrix, outlying cells (entries) in a row are found by the cellHandler technique which…
Pooled variable scaling for cluster analysis
Jakob Raymaekers, Ruben H. Zamar
We propose a new approach for scaling prior to cluster analysis based on the concept of pooled variance. Unlike available scaling procedures such as the standard deviation and the…