通过偏度-峰度集合进行推断

Chris A. J. Klaassen, Bert van Es
{"title":"通过偏度-峰度集合进行推断","authors":"Chris A. J. Klaassen, Bert van Es","doi":"arxiv-2312.06212","DOIUrl":null,"url":null,"abstract":"Kurtosis minus squared skewness is bounded from below by 1, but for unimodal\ndistributions this parameter is bounded by 189/125. In some applications it is\nnatural to compare distributions by comparing their\nkurtosis-minus-squared-skewness parameters. The asymptotic behavior of the\nempirical version of this parameter is studied here for i.i.d. random\nvariables. The result may be used to test the hypothesis of unimodality against\nthe alternative that the kurtosis-minus-squared-skewness parameter is less than\n189/125. However, such a test has to be applied with care, since this parameter\ncan take arbitrarily large values, also for multimodal distributions. Numerical\nresults are presented and for three classes of distributions the\nskewness-kurtosis sets are described in detail.","PeriodicalId":501330,"journal":{"name":"arXiv - MATH - Statistics Theory","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inference via the Skewness-Kurtosis Set\",\"authors\":\"Chris A. J. Klaassen, Bert van Es\",\"doi\":\"arxiv-2312.06212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Kurtosis minus squared skewness is bounded from below by 1, but for unimodal\\ndistributions this parameter is bounded by 189/125. In some applications it is\\nnatural to compare distributions by comparing their\\nkurtosis-minus-squared-skewness parameters. The asymptotic behavior of the\\nempirical version of this parameter is studied here for i.i.d. random\\nvariables. The result may be used to test the hypothesis of unimodality against\\nthe alternative that the kurtosis-minus-squared-skewness parameter is less than\\n189/125. However, such a test has to be applied with care, since this parameter\\ncan take arbitrarily large values, also for multimodal distributions. Numerical\\nresults are presented and for three classes of distributions the\\nskewness-kurtosis sets are described in detail.\",\"PeriodicalId\":501330,\"journal\":{\"name\":\"arXiv - MATH - Statistics Theory\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - MATH - Statistics Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2312.06212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - MATH - Statistics Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2312.06212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

峰度减平方斜度的下界为 1,但对于单模态分布,该参数的下界为 189/125。在某些应用中,通过比较峰度减平方斜度参数来比较分布是很自然的。这里研究的是 i.i.d. 随机变量中该参数经验版本的渐近行为。研究结果可用于检验单模态假设与峰度-减平方-斜度参数小于 189/125 的替代假设。不过,这种检验必须小心谨慎,因为该参数可以任意取大值,多模态分布也是如此。文中给出了数值结果,并详细描述了三类分布的峰度-斜度集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Inference via the Skewness-Kurtosis Set
Kurtosis minus squared skewness is bounded from below by 1, but for unimodal distributions this parameter is bounded by 189/125. In some applications it is natural to compare distributions by comparing their kurtosis-minus-squared-skewness parameters. The asymptotic behavior of the empirical version of this parameter is studied here for i.i.d. random variables. The result may be used to test the hypothesis of unimodality against the alternative that the kurtosis-minus-squared-skewness parameter is less than 189/125. However, such a test has to be applied with care, since this parameter can take arbitrarily large values, also for multimodal distributions. Numerical results are presented and for three classes of distributions the skewness-kurtosis sets are described in detail.
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
期刊最新文献
Precision-based designs for sequential randomized experiments Strang Splitting for Parametric Inference in Second-order Stochastic Differential Equations Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection Tuning parameter selection in econometrics Limiting Behavior of Maxima under Dependence
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1