Alvaro Alexander Burbano-Moreno, Vinícius Diniz Mayrink
{"title":"利用 B 样条对不规则域上的空间函数数据进行高斯建模","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":"{\"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}","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}
Gaussian modeling with B-splines for spatial functional data on irregular domains
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,...
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
