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- Title
Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling.
- Authors
Alizadeh Ghajari, Zakaria; Zare, Karim; Shokri, Soheil
- 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.
- Subjects
MARKOV chain Monte Carlo; GIBBS sampling; REGRESSION analysis; EXPECTATION-maximization algorithms
- Publication
Monte Carlo Methods & Applications, 2024, Vol 30, Issue 2, p137
- ISSN
0929-9629
- Publication type
Article
- DOI
10.1515/mcma-2024-2003