Regularized reduced rank regression for mixed predictor and response variables

British Journal of Mathematical and Statistical Psychology

Abstract

Abstract In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR 4 ), an extension of the GMR 3 model designed to improve performance in high‐dimensional settings. GMR 3 is a regression method for a mix of numeric, binary and ordinal response variables, while also allowing for mixed‐type predictors through optimal scaling. GMR 4 extends this approach by incorporating regularization techniques, such as Ridge, Lasso, Group Lasso, or any combination thereof, making the model suitable for datasets with a large number of predictors or collinearity among them. In addition, we propose a cross‐validation procedure that enables the estimation of the rank and the penalty parameter . Through a simulation study, we evaluate the performance of the model under different scenarios, varying the sample size, the number of non‐informative predictors and response dimension. The results of the simulation study guide the choice of the penalty parameter in the empirical application ISSP: Health and Healthcare I–II (2023), which includes mixed‐type predictors and ordinal responses. In this application, the model results in a sparse and interpretable solution, with a limited set of influential predictors that provide insights into public attitudes towards health care.

Authors 3

1. Lorenza Cotugno corresponding

   University of Naples Federico II

   Affiliation as printed

   University of Naples Federico II Naples Italy

2. Mark de Rooij Aachen

   Leiden University

   Affiliation as printed

   Leiden University Leiden The Netherlands

3. Roberta Siciliano

   University of Naples Federico II

   Affiliation as printed

   University of Naples Federico II Naples Italy