60 citations · 361 across the 33 of their papers we have counts for
21 papers · 1 filter
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…
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.…
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…
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…
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…
Real-time outlier detection for large datasets by RT-DetMCD
Bart De Ketelaere, Mia Hubert, Jakob Raymaekers +2
Modern industrial machines can generate gigabytes of data in seconds, frequently pushing the boundaries of available computing power. Together with the time criticality of industri…
A generalized spatial sign covariance matrix
Jakob Raymaekers, Peter J. Rousseeuw
The well-known spatial sign covariance matrix (SSCM) carries out a radial transform which moves all data points to a sphere, followed by computing the classical covariance matrix o…
Discussion of "The power of monitoring"
Jakob Raymaekers, Peter J. Rousseeuw, Iwein Vranckx
This is an invited comment on the discussion paper "The power of monitoring: how to make the most of a contaminated multivariate sample" by A. Cerioli, M. Riani, A. Atkinson and A.…
Fast robust correlation for high-dimensional data
Jakob Raymaekers, Peter J. Rousseeuw
The product moment covariance is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other res…
A Measure of Directional Outlyingness with Applications to Image Data and Video
Peter J. Rousseeuw, Jakob Raymaekers, Mia Hubert
Functional data covers a wide range of data types. They all have in common that the observed objects are functions of of a univariate argument (e.g. time or wavelength) or a multiv…
Finding Outliers in Surface Data and Video
Mia Hubert, Jakob Raymaekers, Peter J. Rousseeuw +1
Surface, image and video data can be considered as functional data with a bivariate domain. To detect outlying surfaces or images, a new method is proposed based on the mean and th…