Gaussian modeling with B-splines for spatial functional data on irregular domains

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY Statistics Pub Date : 2024-09-13 DOI:10.1080/02331888.2024.2402069
Alvaro Alexander Burbano-Moreno, Vinícius Diniz Mayrink
{"title":"Gaussian modeling with B-splines for spatial functional data on irregular domains","authors":"Alvaro Alexander Burbano-Moreno, Vinícius Diniz Mayrink","doi":"10.1080/02331888.2024.2402069","DOIUrl":null,"url":null,"abstract":"Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats data as functions,...","PeriodicalId":54358,"journal":{"name":"Statistics","volume":"200 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/02331888.2024.2402069","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

Abstract

Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats data as functions,...
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
利用 B 样条对不规则域上的空间函数数据进行高斯建模
函数数据分析(FDA)已成为一种强大的框架,适用于在指定区间内呈现连续变化的数据集。与传统方法不同,FDA 将数据视为函数,...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Statistics
Statistics 数学-统计学与概率论
CiteScore
1.00
自引率
0.00%
发文量
59
审稿时长
12 months
期刊介绍: Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.
期刊最新文献
Robust estimator of the ruin probability in infinite time for heavy-tailed distributions Gaussian modeling with B-splines for spatial functional data on irregular domains A note on the asymptotic behavior of a mildly unstable integer-valued AR(1) model Explainable machine learning for financial risk management: two practical use cases Online updating Huber robust regression for big data streams
×
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