5 papers
The Regularization Parameter: Sparse Precision Matrix Estimation
Aryan Eftekhari, Daniel Sergio Vega, Ernst-Jan Camiel Wit +1
Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data…
Loglinear modelling of huge contingency tables
Veronica Vinciotti, Ernst C. Wit
Contingency tables are the canonical representation of multivariate categorical data. As the size of the contingency table grows exponentially with the number of variables, even a…
Functional structural equation models with out-of-sample guarantees
Philip Kennerberg, Ernst C. Wit
Statistical learning methods typically assume that the training and test data originate from the same distribution, enabling effective risk minimization. However, real-world applic…
Functional worst risk minimization
Philip Kennerberg, Ernst C. Wit
The aim of this paper is to extend worst risk minimization, also called worst average loss minimization, to the functional realm. This means finding a functional regression represe…
Constructive and consistent estimation of quadratic minimax
Philip Kennerberg, Ernst C. Wit
We consider square integrable random variables and random (row) vectors of length , such that is square integrable for …