Heteroscedastic Laplace mixture of experts regression models and applications
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.
关键词：
mixture of experts regression models,
heteroscedastic mixture of experts regression models,
Laplace distribution,
MM algorithm
