# Moment-based Estimation of Mixtures of Regression Models

## TL;DR

This paper introduces a moment-based method for estimating parameters in finite mixture of regression models, requiring minimal assumptions and demonstrating high accuracy through simulations and real data application.

## Contribution

It develops unbiased, assumption-light estimators for mixture regression models, expanding the toolkit for flexible modeling without distributional constraints.

## Key findings

- Estimators are unbiased and consistent.
- Method performs well in simulations.
- Application to wine data demonstrates practical utility.

## Abstract

Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data.