6 papers · 1 filter
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…