New estimation in mixture of experts models using the Pearson type VII distribution. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Yin, Junhui; Wu, Liucang; Lu, Hanchi; Dai, Lin
• Is Part Of: Communications in statistics. Volume 49:Issue 2(2020)
• Page Start: 472
• Page End: 483
• Publication Date: 2020-02-01
• Subjects: EM algorithm -- mixture of experts -- Pearson type VII distribution -- robust estimation; 62F35 -- 62H30 -- 62J07; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2018.1485943
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: Assuming that the error terms follow a Pearson type VII distribution, we propose a new estimation in mixture of experts models by adding an l 2 − norm of the regression coefficients of the mixing proportions to the log-likelihood function. This l 2 -penalized maximum likelihood estimator is a root-n consistent estimator of the true parameter vector, and its finite sample behaviour is better than that of the ordinary maximum likelihood estimator. An efficient EM algorithm is suggested for the inference, and the methodology is illustrated through some simulation and comparison studies. An application of the proposed method to a data set is described.