A. Szczepańska-Álvarez, B. Zawieja, Adolfo Álvarez
{"title":"具有可分离协方差矩阵结构的多元线性模型的最大似然算法的性质","authors":"A. Szczepańska-Álvarez, B. Zawieja, Adolfo Álvarez","doi":"10.2478/bile-2021-0005","DOIUrl":null,"url":null,"abstract":"Summary In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect the observations. Additionally, one process is partially modeled by a compound symmetry structure. We perform a simulation study of the properties of an iteratively determined estimator of the covariance matrix.","PeriodicalId":8933,"journal":{"name":"Biometrical Letters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Properties of an MLE algorithm for the multivariate linear model with a separable covariance matrix structure\",\"authors\":\"A. Szczepańska-Álvarez, B. Zawieja, Adolfo Álvarez\",\"doi\":\"10.2478/bile-2021-0005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect the observations. Additionally, one process is partially modeled by a compound symmetry structure. We perform a simulation study of the properties of an iteratively determined estimator of the covariance matrix.\",\"PeriodicalId\":8933,\"journal\":{\"name\":\"Biometrical Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biometrical Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/bile-2021-0005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biometrical Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/bile-2021-0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Properties of an MLE algorithm for the multivariate linear model with a separable covariance matrix structure
Summary In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect the observations. Additionally, one process is partially modeled by a compound symmetry structure. We perform a simulation study of the properties of an iteratively determined estimator of the covariance matrix.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
