等级阿基米德联结的惩罚估计

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis Pub Date : 2023-11-29 DOI:10.1016/j.jmva.2023.105274

Ostap Okhrin , Alexander Ristig

{"title":"等级阿基米德联结的惩罚估计","authors":"Ostap Okhrin , Alexander Ristig","doi":"10.1016/j.jmva.2023.105274","DOIUrl":null,"url":null,"abstract":"<div><p>This manuscript discusses a novel estimation approach for parametric hierarchical Archimedean copula. The parameters and structure of this copula are simultaneously estimated while imposing a non-concave penalty on differences between parameters which coincides with an implicit penalty on the copula’s structure. The asymptotic properties of the resulting penalized estimator are studied and small sample properties are illustrated using simulations.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X23001203/pdfft?md5=1aee43f0a4042437779957fee35e851c&pid=1-s2.0-S0047259X23001203-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Penalized estimation of hierarchical Archimedean copula\",\"authors\":\"Ostap Okhrin , Alexander Ristig\",\"doi\":\"10.1016/j.jmva.2023.105274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This manuscript discusses a novel estimation approach for parametric hierarchical Archimedean copula. The parameters and structure of this copula are simultaneously estimated while imposing a non-concave penalty on differences between parameters which coincides with an implicit penalty on the copula’s structure. The asymptotic properties of the resulting penalized estimator are studied and small sample properties are illustrated using simulations.</p></div>\",\"PeriodicalId\":16431,\"journal\":{\"name\":\"Journal of Multivariate Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0047259X23001203/pdfft?md5=1aee43f0a4042437779957fee35e851c&pid=1-s2.0-S0047259X23001203-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Multivariate Analysis\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0047259X23001203\",\"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/S0047259X23001203","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

本文讨论了一种新的参数层次阿基米德联结估计方法。同时估计了该联结体的参数和结构，并对参数之间的差异施加非凹惩罚，同时对联结体的结构施加隐式惩罚。研究了所得到的惩罚估计量的渐近性质，并用仿真说明了小样本性质。

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

Penalized estimation of hierarchical Archimedean copula

This manuscript discusses a novel estimation approach for parametric hierarchical Archimedean copula. The parameters and structure of this copula are simultaneously estimated while imposing a non-concave penalty on differences between parameters which coincides with an implicit penalty on the copula’s structure. The asymptotic properties of the resulting penalized estimator are studied and small sample properties are illustrated using simulations.

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.