{"title":"关于多元相关系数的构造","authors":"J. Merker, Gregor Schuldt","doi":"10.1145/3409915.3409920","DOIUrl":null,"url":null,"abstract":"A multivariate (un)correlation coefficient maps a vector-valued random variable X = (X1,... XN) to a real number between 0 and 1, which indicates how linearly (un)correlated its components Xi, i = 1,..., N, are. In this paper, we provide a unified framework for multivariate (un)correlation coefficients known in literature, and construct new multivariate (un)correlation coefficients using Rényi entropies, which allow applications in many scientific areas.","PeriodicalId":114746,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Mathematics and Statistics","volume":"136 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On the Construction of Multivariate Correlation Coefficients\",\"authors\":\"J. Merker, Gregor Schuldt\",\"doi\":\"10.1145/3409915.3409920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multivariate (un)correlation coefficient maps a vector-valued random variable X = (X1,... XN) to a real number between 0 and 1, which indicates how linearly (un)correlated its components Xi, i = 1,..., N, are. In this paper, we provide a unified framework for multivariate (un)correlation coefficients known in literature, and construct new multivariate (un)correlation coefficients using Rényi entropies, which allow applications in many scientific areas.\",\"PeriodicalId\":114746,\"journal\":{\"name\":\"Proceedings of the 2020 3rd International Conference on Mathematics and Statistics\",\"volume\":\"136 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 3rd International Conference on Mathematics and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3409915.3409920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 3rd International Conference on Mathematics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3409915.3409920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Construction of Multivariate Correlation Coefficients
A multivariate (un)correlation coefficient maps a vector-valued random variable X = (X1,... XN) to a real number between 0 and 1, which indicates how linearly (un)correlated its components Xi, i = 1,..., N, are. In this paper, we provide a unified framework for multivariate (un)correlation coefficients known in literature, and construct new multivariate (un)correlation coefficients using Rényi entropies, which allow applications in many scientific areas.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
