评论基于形状的功能数据分析

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY Test Pub Date : 2024-01-18 DOI:10.1007/s11749-023-00914-6
J. E. Borgert, J. S. Marron
{"title":"评论基于形状的功能数据分析","authors":"J. E. Borgert, J. S. Marron","doi":"10.1007/s11749-023-00914-6","DOIUrl":null,"url":null,"abstract":"<p>This discussion paper applauds the authors for their impactful contribution to functional data analysis (FDA). Their primary insight lies in a formal mathematical definition of the “shape” of a curve, which they connect to familiar intuitive notions through a number of examples. Notably, the paper highlights the pitfalls of less well-thought-out curve registration approaches. The authors’ application of COVID-19 data enriches the discussion, highlighting the work’s practical relevance. We discuss connections of this work with object-oriented data analysis and propose enhancements to the authors’ shape-based functional principal component analysis. Additionally, we illustrate the practical significance of adaptive alignment with an example from our own research.</p>","PeriodicalId":51189,"journal":{"name":"Test","volume":"11 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comments on: Shape-based functional data analysis\",\"authors\":\"J. E. Borgert, J. S. Marron\",\"doi\":\"10.1007/s11749-023-00914-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This discussion paper applauds the authors for their impactful contribution to functional data analysis (FDA). Their primary insight lies in a formal mathematical definition of the “shape” of a curve, which they connect to familiar intuitive notions through a number of examples. Notably, the paper highlights the pitfalls of less well-thought-out curve registration approaches. The authors’ application of COVID-19 data enriches the discussion, highlighting the work’s practical relevance. We discuss connections of this work with object-oriented data analysis and propose enhancements to the authors’ shape-based functional principal component analysis. Additionally, we illustrate the practical significance of adaptive alignment with an example from our own research.</p>\",\"PeriodicalId\":51189,\"journal\":{\"name\":\"Test\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Test\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s11749-023-00914-6\",\"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":"Test","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11749-023-00914-6","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0

摘要

本讨论稿对作者为函数数据分析 (FDA) 所做的有影响力的贡献表示赞赏。他们的主要见解在于对曲线 "形状 "的正式数学定义,并通过大量实例将其与熟悉的直观概念联系起来。值得注意的是,论文强调了一些考虑不周的曲线注册方法存在的缺陷。作者对 COVID-19 数据的应用丰富了讨论内容,突出了这项工作的实用性。我们讨论了这项工作与面向对象数据分析的联系,并对作者基于形状的功能主成分分析提出了改进建议。此外,我们还以自己的研究为例,说明了自适应配准的实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Comments on: Shape-based functional data analysis

This discussion paper applauds the authors for their impactful contribution to functional data analysis (FDA). Their primary insight lies in a formal mathematical definition of the “shape” of a curve, which they connect to familiar intuitive notions through a number of examples. Notably, the paper highlights the pitfalls of less well-thought-out curve registration approaches. The authors’ application of COVID-19 data enriches the discussion, highlighting the work’s practical relevance. We discuss connections of this work with object-oriented data analysis and propose enhancements to the authors’ shape-based functional principal component analysis. Additionally, we illustrate the practical significance of adaptive alignment with an example from our own research.

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
Test
Test 数学-统计学与概率论
CiteScore
2.20
自引率
7.70%
发文量
41
审稿时长
>12 weeks
期刊介绍: TEST is an international journal of Statistics and Probability, sponsored by the Spanish Society of Statistics and Operations Research. English is the official language of the journal. The emphasis of TEST is placed on papers containing original theoretical contributions of direct or potential value in applications. In this respect, the methodological contents are considered to be crucial for the papers published in TEST, but the practical implications of the methodological aspects are also relevant. Original sound manuscripts on either well-established or emerging areas in the scope of the journal are welcome. One volume is published annually in four issues. In addition to the regular contributions, each issue of TEST contains an invited paper from a world-wide recognized outstanding statistician on an up-to-date challenging topic, including discussions.
期刊最新文献
Jackknife empirical likelihood for the correlation coefficient with additive distortion measurement errors Nonparametric conditional survival function estimation and plug-in bandwidth selection with multiple covariates Higher-order spatial autoregressive varying coefficient model: estimation and specification test Composite quantile estimation in partially functional linear regression model with randomly censored responses Bayesian inference and cure rate modeling for event history data
×
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