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

Alizadeh Ghajari Zakaria (), Zare Karim () and Shokri Soheil ()
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Alizadeh Ghajari Zakaria: Department of Statistics, 201560 Marvdasht Branch, Islamic Azad University , Marvdasht, Iran
Zare Karim: Department of Statistics, 201560 Marvdasht Branch, Islamic Azad University , Marvdasht, Iran
Shokri Soheil: Department of Mathematics, 201524 Rasht Branch, Islamic Azad University , Rasht, Iran

Monte Carlo Methods and Applications, 2024, vol. 30, issue 2, 137-148

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.

Keywords: EM-type algorithm; Gibbs sampling; linear regression models; MCMC method; shape mixtures of skew-normal distribution (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/mcma-2024-2003

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