Heteroscedastic Laplace mixture of experts regression models and applications

Abstract

Mixture of Experts (MoE) regression models are widely studied in statistics and machine learning for modeling heterogeneity in data for regression, clustering and classification. Laplace distribution is one of the most important statistical tools to analyze thick and tail data. Laplace Mixture of Linear Experts (LMoLE) regression models are based on the Laplace distribution which is more robust. Similar to modelling variance parameter in a homogeneous population, we propose and study a new novel class of models: heteroscedastic Laplace mixture of experts regression models to analyze the heteroscedastic data coming from a heterogeneous population in this paper. The issues of maximum likelihood estimation are addressed. In particular, Minorization-Maximization (MM) algorithm for estimating the regression parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo simulations. Results from the analysis of two real data sets are presented.