通过与吉布斯抽样相结合的 ECM 在倾斜正态线性回归模型的形状混合物中进行估计

Pub Date : 2024-04-11 DOI:10.1515/mcma-2024-2003
Zakaria Alizadeh Ghajari, Karim Zare, Soheil Shokri
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引用次数: 0

摘要

在本文中,我们研究了误差项具有偏态正态分布形状混合物的线性回归模型。这种分布属于偏态正态分布(SN)类,可用于重尾和不对称数据。对于 SN 系列参数的经典(非贝叶斯)估计,我们首次应用了马尔可夫链蒙特卡罗 ECM(MCMC-ECM)算法,其中样本由吉布斯抽样生成,称为吉布斯-ECM,同时,我们还针对上述模型扩展了 EM 算法的其他两种类型。最后,我们通过模拟对所提出的方法进行了评估,并使用真实数据集将其与 Numerical Math-ECM 算法和蒙特卡罗 ECM(MC-ECM)进行了比较。
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Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling
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.
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