8 citations · 19 across the 15 of their papers we have counts for
10 papers · 1 filter
Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares
V. Nesrstová, I. Wilms, J. Palarea-Albaladejo +4
High-dimensional compositional data are commonplace in the modern omics sciences amongst others. Analysis of compositional data requires a proper choice of orthonormal coordinate r…
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…
Regularized Predictive Models for Beef Eating Quality of Individual Meals
Garth Tarr, Ines Wilms
Faced with changing markets and evolving consumer demands, beef industries are investing in grading systems to maximise value extraction throughout their entire supply chain. The M…
Graphical Influence Diagnostics for Changepoint Models
Ines Wilms, Rebecca Killick, David S. Matteson
Changepoint models enjoy a wide appeal in a variety of disciplines to model the heterogeneity of ordered data. Graphical influence diagnostics to characterize the influence of sing…
Tree-based Node Aggregation in Sparse Graphical Models
Ines Wilms, Jacob Bien
High-dimensional graphical models are often estimated using regularization that is aimed at reducing the number of edges in a network. In this work, we show how even simpler networ…
Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages
Ines Wilms, Sumanta Basu, Jacob Bien +1
The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon i…
Cellwise robust regularized discriminant analysis
Stéphanie Aerts, Ines Wilms
Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each gro…
Sparse cointegration
Ines Wilms, Christophe Croux
Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegr…
Robust Sparse Canonical Correlation Analysis
Ines Wilms, Christophe Croux
Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinatio…
Sparse canonical correlation analysis from a predictive point of view
Ines Wilms, Christophe Croux
Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each dat…