{"title":"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","authors":"Zakaria Alizadeh Ghajari, Karim Zare, Soheil Shokri","doi":"10.1515/mcma-2024-2003","DOIUrl":null,"url":null,"abstract":"\n In this paper, we study linear regression models in which the error term has shape mixtures of skew-normal distribution.\nThis type of distribution belongs to the skew-normal (SN) distribution class that can be used for heavy tails and asymmetry data.\nFor 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.\nFinally, 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.","PeriodicalId":46576,"journal":{"name":"Monte Carlo Methods and Applications","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling\",\"authors\":\"Zakaria Alizadeh Ghajari, Karim Zare, Soheil Shokri\",\"doi\":\"10.1515/mcma-2024-2003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In this paper, we study linear regression models in which the error term has shape mixtures of skew-normal distribution.\\nThis type of distribution belongs to the skew-normal (SN) distribution class that can be used for heavy tails and asymmetry data.\\nFor 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.\\nFinally, 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.\",\"PeriodicalId\":46576,\"journal\":{\"name\":\"Monte Carlo Methods and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Monte Carlo Methods and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/mcma-2024-2003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Monte Carlo Methods and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/mcma-2024-2003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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。
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.