16 citations · 38 across the 11 of their papers we have counts for
12 papers
Smooth Reduced Rank Regression with P-splines
Mark de Rooij
Linear regression is one of the core statistical tools used for analysis of data. In the era of statistical learning, linear regression has been expanded into two directions. The f…
Principal Covariate Regression with Nuclear Norm Penalty
Kaiwen Liu, Lisa Verbeij, Wouter Weeda +1
In high-dimensional data settings, dimensionality reduction or variable selection are key steps when using statistical learning techniques. Principal Covariate Regression-type meth…
Regularized Reduced Rank Regression for mixed predictor and response variables
Lorenza Cotugno, Mark de Rooij, Roberta Siciliano
In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR4), an extension of the GMR3 model designed to improve performance in high-dimension…
Reduced Rank Regression for Mixed Predictor and Response Variables
Mark de Rooij, Lorenza Cotugno, Roberta Siciliano
In this paper, we propose the generalized mixed reduced rank regression method, GMR for short. GMR is a regression method for a mix of numeric, binary, and ordinal response…
A Multinomial Canonical Decomposition Model, with emphasis on the analysis of Multivariate Binary data
Mark de Rooij
In this paper, we propose to decompose the canonical parameter of a multinomial model into a set of participant scores and category scores. External information about the participa…
Logistic Multidimensional Data Analysis for Ordinal Response Variables using a Cumulative Link function
Mark de Rooij, Ligaya Breemer, Dion Woestenburg +1
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, lead…
Supervised and Unsupervised Mapping of Binary Variables: A proximity perspective
Mark de Rooij, Dion Woestenburg, Frank Busing
We propose a new mapping tool for supervised and unsupervised analysis of multivariate binary data with multiple items, questions, or response variables. The mapping assumes an und…
Continuous Sweep for Binary Quantification Learning
Kevin Kloos, Julian D. Karch, Quinten A. Meertens +1
A quantifier is a supervised machine learning algorithm, focused on estimating the class prevalence in a dataset rather than labeling its individual observations. We introduce Cont…
Imputation of missing values in multi-view data
Wouter van Loon, Marjolein Fokkema, Frank de Vos +3
Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all feature…
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
Wouter van Loon, Frank de Vos, Marjolein Fokkema +4
Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (S…
The MELODIC family for simultaneous binary logistic regression in a reduced space
Mark de Rooij, Patrick J. F. Groenen
Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and simultaneous analysis is beneficial…
View selection in multi-view stacking: Choosing the meta-learner
Wouter van Loon, Marjolein Fokkema, Botond Szabo +1
Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learne…