# Variable selection in discriminant analysis for mixed variables and several groups

## TL;DR

This paper introduces a new variable selection method for discriminant analysis involving mixed categorical and continuous variables, utilizing a criterion that simplifies the selection process and proves consistency through simulations.

## Contribution

The paper presents a novel, consistent variable selection approach for mixed-variable discriminant analysis, reducing the problem to permutation and dimensionality estimation.

## Key findings

- Method is consistent based on theoretical proof.
- Simulation study compares favorably with existing methods.
- Approach effectively handles mixed categorical and continuous variables.

## Abstract

We propose a method for variable selection in discriminant analysis with mixed categorical and continuous variables. This method is based on a criterion that permits to reduce the variable selection problem to a problem of estimating suitable permutation and dimensionality. Then, estimators for these parameters are proposed and the resulting method for selecting variables is shown to be consistent. A simulation study that permits to study several poperties of the proposed approach and to compare it with an existing method is given.