Mixture regression modelling based on the shape mixtures of skew Laplace normal distribution


Doğru F. Z., ARSLAN O.

Journal of Statistical Computation and Simulation, vol.93, no.18, pp.3403-3420, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 93 Issue: 18
  • Publication Date: 2023
  • Doi Number: 10.1080/00949655.2023.2226281
  • Journal Name: Journal of Statistical Computation and Simulation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3403-3420
  • Keywords: EM algorithm, mixture regression model, ML, SMSLN, SMSTN
  • Karadeniz Technical University Affiliated: No

Abstract

Modelling skewness and heavy-tailedness in heterogeneous data sets is a compelling problem, particularly in regression analysis. The main goal of this study is to propose a mixture regression model based on the shape mixtures of skew Laplace normal (SMSLN) distribution for modelling skewness and heavy-tailedness simultaneously. The SMSLN distribution has been introduced by Doğru and Arslan [Finite mixtures of skew Laplace normal distributions with random skewness. 11th International Statistics Congress (ISC2019); Bodrum/Turkey; Finite mixtures of skew Laplace normal distributions with random skewness. Comput Stat. 2021;36(1):423–447] as a flexible extension of the skew Laplace normal (SLN) distribution and includes an extra shape parameter that controls skewness and kurtosis. The maximum likelihood estimators for the parameters of interest with the help of the expectation-maximization (EM) algorithm are obtained. The performance of the proposed mixture model is demonstrated via a simulation study and a real data example.