Heteroscedastic Laplace mixture of experts regression models and applications  

作　　者：WU Liu-cang ZHANG Shu-yu LI Shuang-shuang 

机构地区：[1]Faculty of Science,Kunming University of Science and Technology,Kunming 650093,China [2]College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350117,China

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2021年第1期60-69,共10页高校应用数学学报（英文版）（B辑）

基　　金：the National Natural Science Foundation of China(11861041,11261025).

摘　　要：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 

分 类 号：O212.1[理学—概率论与数理统计]