Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

Dogru F. Z., ARSLAN O.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.46, no.21, pp.10879-10896, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 21
  • Publication Date: 2017
  • Doi Number: 10.1080/03610926.2016.1252400
  • Journal Name: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.10879-10896
  • Keywords: EM algorithm, mixture model, mixture regression model, ML, SLN, STN, 62F35, 62H12, 65C20, 68U20, MAXIMUM-LIKELIHOOD, MULTIVARIATE, EM
  • Ankara University Affiliated: Yes

Abstract

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation-maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.