Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling-Reference-Cited by-同舟云学术

Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling

Published:2024-04-11 Issue:2 Volume:30 Page:137-148
ISSN:0929-9629
Container-title:Monte Carlo Methods and Applications
language:en
Short-container-title:

Author:

Alizadeh Ghajari Zakaria¹,Zare Karim¹^ORCID,Shokri Soheil²^ORCID

Affiliation:

1. Department of Statistics , 201560 Marvdasht Branch, Islamic Azad University , Marvdasht , Iran

2. Department of Mathematics , 201524 Rasht Branch, Islamic Azad University , Rasht , Iran

Abstract

Abstract In this paper, we study linear regression models in which the error term has shape mixtures of skew-normal distribution. This type of distribution belongs to the skew-normal (SN) distribution class that can be used for heavy tails and asymmetry data. For the first time, for the classical (non-Bayesian) estimation of the parameters of the SN family, we apply the Markov chains Monte Carlo ECM (MCMC-ECM) algorithm where the samples are generated by Gibbs sampling, denoted by Gibbs-ECM, and also, we extend two other types of the EM algorithm for the above model. Finally, the proposed method is evaluated through a simulation and compared with the Numerical Math-ECM algorithm and Monte Carlo ECM (MC-ECM) using a real data set.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/mcma-2024-2003/pdf

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