{"title":"RR分类器:一种基于相对秩的多维空间非参数分类方法","authors":"Ondrej Vencalek, Olusola Samuel Makinde","doi":"10.1007/s10182-021-00423-7","DOIUrl":null,"url":null,"abstract":"<div><p>Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.\n</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks\",\"authors\":\"Ondrej Vencalek, Olusola Samuel Makinde\",\"doi\":\"10.1007/s10182-021-00423-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.\\n</p></div>\",\"PeriodicalId\":55446,\"journal\":{\"name\":\"Asta-Advances in Statistical Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2021-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asta-Advances in Statistical Analysis\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10182-021-00423-7\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asta-Advances in Statistical Analysis","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10182-021-00423-7","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks
Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.
期刊介绍:
AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.