11 citations · 28 across the 6 of their papers we have counts for
10 papers
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…