{"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":"201 ","pages":"Article 105274"},"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}
引用次数: 0
Abstract
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.
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.
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