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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alizadeh Ghajari Zakaria & Zare Karim & Shokri Soheil, 2024. "Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling," Monte Carlo Methods and Applications, De Gruyter, vol. 30(2), pages 137-148.
    • Handle: RePEc:bpj:mcmeap:v:30:y:2024:i:2:p:137-148:n:1006
    DOI: 10.1515/mcma-2024-2003
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