考虑到统计学中的距离度量

C. Kitsos, C. Nisiotis
{"title":"考虑到统计学中的距离度量","authors":"C. Kitsos, C. Nisiotis","doi":"10.2478/bile-2022-0006","DOIUrl":null,"url":null,"abstract":"Summary The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on this fact we propose a new step, to adopt from Information Theory, the divergence measures, as distance methods, to compare two distributions, and not only to investigate if the means or the variances of the distributions are equal. Some useful results towards this line of thought are presented, adopting a compact form for all known divergence measures, and are appropriately analyzed for Biometrical, and not only, applications.","PeriodicalId":8933,"journal":{"name":"Biometrical Letters","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Considering distance measures in Statistics\",\"authors\":\"C. Kitsos, C. Nisiotis\",\"doi\":\"10.2478/bile-2022-0006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on this fact we propose a new step, to adopt from Information Theory, the divergence measures, as distance methods, to compare two distributions, and not only to investigate if the means or the variances of the distributions are equal. Some useful results towards this line of thought are presented, adopting a compact form for all known divergence measures, and are appropriately analyzed for Biometrical, and not only, applications.\",\"PeriodicalId\":8933,\"journal\":{\"name\":\"Biometrical Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biometrical Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/bile-2022-0006\",\"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-2022-0006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文的目的是提供一个紧凑的审查所谓的距离方法在统计学中,其中包括所有已知的估计方法。基于这一事实,我们提出了一个新的步骤,即从信息论中采用发散度量,作为距离方法,来比较两个分布,而不仅仅是研究分布的均值或方差是否相等。对这条思路提出了一些有用的结果,对所有已知的散度测量采用紧凑的形式,并适当地分析了生物识别,而不仅仅是应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Considering distance measures in Statistics
Summary The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on this fact we propose a new step, to adopt from Information Theory, the divergence measures, as distance methods, to compare two distributions, and not only to investigate if the means or the variances of the distributions are equal. Some useful results towards this line of thought are presented, adopting a compact form for all known divergence measures, and are appropriately analyzed for Biometrical, and not only, applications.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信