25 citations · 57 across the 12 of their papers we have counts for
11 papers · 1 filter
Multi-Model Subset Selection
Anthony-Alexander Christidis, Stefan Van Aelst, Ruben Zamar
The two primary approaches for high-dimensional regression problems are sparse methods (e.g., best subset selection, which uses the L0-norm in the penalty) and ensemble methods (e.…
Robust and efficient estimation of nonparametric generalized linear models
Ioannis Kalogridis, Gerda Claeskens, Stefan Van Aelst
Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and t…
Data-Driven Logistic Regression Ensembles With Applications in Genomics
Anthony-Alexander Christidis, Stefan Van Aelst, Ruben Zamar
Advances in data collecting technologies in genomics have significantly increased the need for tools designed to study the genetic basis of many diseases. Effective statistical met…
Robust penalized spline estimation with difference penalties
Ioannis Kalogridis, Stefan Van Aelst
Penalized spline estimation with discrete difference penalties (P-splines) is a popular estimation method for semiparametric models, but the classical least-squares estimator is hi…
Robust optimal estimation of location from discretely sampled functional data
Ioannis Kalogridis, Stefan Van Aelst
Estimating location is a central problem in functional data analysis, yet most current estimation procedures either unrealistically assume completely observed trajectories or lack…
Robust penalized estimators for functional linear regression
Ioannis Kalogridis, Stefan Van Aelst
Functional data analysis is a fast evolving branch of statistics. Estimation procedures for the popular functional linear model either suffer from lack of robustness or are computa…
Objective-Driven Ensembles: Bridging the Gap Between Interpretable Sparsity and Algorithmic Prediction
Anthony Christidis, Stefan Van Aelst, Ruben Zamar
Sparse methods (e.g., Best Subset Selection, Elastic Net) are the standard approach for obtaining interpretable models, but they can suffer from high variance and vulnerability to…
Robust Inference for Seemingly Unrelated Regression Models
Kris Peremans, Stefan Van Aelst
Seemingly unrelated regression models generalize linear regression models by considering multiple regression equations that are linked by contemporaneously correlated disturbances.…
Robust variable screening for regression using factor profiling
Yixin Wang, Stefan Van Aelst
Sure Independence Screening is a fast procedure for variable selection in ultra-high dimensional regression analysis. Unfortunately, its performance greatly deteriorates with incre…
Robust bootstrap procedures for the chain-ladder method
Kris Peremans, Pieter Segaert, Stefan Van Aelst +1
Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used te…
On the consistency of a spatial-type interval-valued median for random intervals
Beatriz Sinova, Stefan Van Aelst
The sample -median is a robust estimator of the central tendency or location of an interval-valued random variable. While the interval-valued sample mean can be highly influen…