具有局部极值的矩阵值各向同性协方差函数

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis Pub Date : 2023-11-18 DOI:10.1016/j.jmva.2023.105250

Alfredo Alegría , Xavier Emery

{"title":"具有局部极值的矩阵值各向同性协方差函数","authors":"Alfredo Alegría , Xavier Emery","doi":"10.1016/j.jmva.2023.105250","DOIUrl":null,"url":null,"abstract":"<div><p>Multivariate random fields are commonly used in spatial statistics<span><span> and natural science to model coregionalized variables. In this context, the matrix-valued covariance function<span> plays a central role in capturing their spatial continuity and interdependence. This study aims to contribute to the literature on covariance modeling by proposing new parametric families of isotropic matrix-valued functions exhibiting non-monotonic behaviors, namely hole effects and cross-dimples. The benefit of the proposed models is shown on a </span></span>bivariate data set consisting of concentrations of airborne particulate matter.</span></p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Matrix-valued isotropic covariance functions with local extrema\",\"authors\":\"Alfredo Alegría , Xavier Emery\",\"doi\":\"10.1016/j.jmva.2023.105250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Multivariate random fields are commonly used in spatial statistics<span><span> and natural science to model coregionalized variables. In this context, the matrix-valued covariance function<span> plays a central role in capturing their spatial continuity and interdependence. This study aims to contribute to the literature on covariance modeling by proposing new parametric families of isotropic matrix-valued functions exhibiting non-monotonic behaviors, namely hole effects and cross-dimples. The benefit of the proposed models is shown on a </span></span>bivariate data set consisting of concentrations of airborne particulate matter.</span></p></div>\",\"PeriodicalId\":16431,\"journal\":{\"name\":\"Journal of Multivariate Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Multivariate Analysis\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0047259X23000969\",\"RegionNum\":3,\"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":"Journal of Multivariate Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0047259X23000969","RegionNum":3,"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

多元随机场常用于空间统计和自然科学中对共区域化变量进行建模。在这种情况下，矩阵值协方差函数在捕捉它们的空间连续性和相互依赖性方面起着核心作用。本研究旨在通过提出具有非单调行为(即空穴效应和交叉凹陷)的各向同性矩阵值函数的新参数族，为协方差建模的文献做出贡献。在由空气中颗粒物浓度组成的双变量数据集上显示了所提出模型的效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Matrix-valued isotropic covariance functions with local extrema

Multivariate random fields are commonly used in spatial statistics and natural science to model coregionalized variables. In this context, the matrix-valued covariance function plays a central role in capturing their spatial continuity and interdependence. This study aims to contribute to the literature on covariance modeling by proposing new parametric families of isotropic matrix-valued functions exhibiting non-monotonic behaviors, namely hole effects and cross-dimples. The benefit of the proposed models is shown on a bivariate data set consisting of concentrations of airborne particulate matter.

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

Journal of Multivariate Analysis 数学-统计学与概率论

CiteScore

2.40

自引率

25.00%

发文量

108

审稿时长

74 days

期刊介绍： Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data. The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of Copula modeling Functional data analysis Graphical modeling High-dimensional data analysis Image analysis Multivariate extreme-value theory Sparse modeling Spatial statistics.