Reduced Rank Regression for Mixed Predictor and Response Variables

arXiv (Cornell University)

Abstract

In this paper, we propose the generalized mixed reduced rank regression method, GMR$^3$ for short. GMR$^3$ is a regression method for a mix of numeric, binary, and ordinal response variables. The predictor variables can be a mix of binary, nominal, ordinal, and numeric variables. For dealing with the categorical predictors we use optimal scaling. A majorization-minimization algorithm is derived for maximum likelihood estimation under a local independence assumption. A series of simulation studies is shown (Section 4) to evaluate the performance of the algorithm with different types of predictor and response variables. In Section 5.2, we briefly discuss the choices to make when applying the model the empirical data and give suggestions for supporting such choices. In Section 6.1, we show an application of GMR$^3$ using the Eurobarometer Surveys data set of 2023.

Authors 3

1. Mark de Rooij Aachen

   Leiden University

   Affiliation as printed

   Methodology and Statistics department Leiden University The Netherlands

2. Lorenza Cotugno

   Federico II University Hospital

   Affiliation as printed

   Department of Physics University of Naples Federico II Italy

3. Roberta Siciliano

   University of Naples Federico II

   Affiliation as printed

   Department of Electrical Engineering and Information Technology University of Naples Federico II Italy