# Robust Model Selection for Finite Mixture of Regression Models Through Trimming

## TL;DR

This paper presents a robust variable selection method for finite mixture of regression models using trimming, which is less sensitive to outliers and improves finite sample performance.

## Contribution

It introduces a novel trimming-based variable selection technique for finite mixture regression models, enhancing robustness over traditional methods.

## Key findings

- The proposed method is robust to outliers.
- Numerical studies show improved finite sample performance.
- Comparison with existing methods demonstrates advantages.

## Abstract

In this article, we introduce a new variable selection technique through trimming for finite mixture of regression models. Compared to the traditional variable selection techniques, the new method is robust and not sensitive to outliers. The estimation algorithm is introduced and numerical studies are conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods.