View in EDS

Model selection for discrete regular vine copulas.

Saved in:
Bibliographic Details
Title: Model selection for discrete regular vine copulas.
Authors: Panagiotelis, Anastasios1 anastasios.panagiotelis@monash.edu, Czado, Claudia2, Joe, Harry3, Stöber, Jakob4
Source: Computational Statistics & Data Analysis. Feb2017, Vol. 106, p138-152. 15p.
Subjects: Copula functions, Discrete systems, High-dimensional model representation, Multivariate analysis, Probability theory, Marginal distributions
Abstract: Discrete vine copulas provide a flexible modeling framework for high-dimensional data and have significant computational advantages over competing methods. A vine-based multivariate probability mass function is constructed from bivariate copula building blocks and univariate marginal distributions. However, even for a moderate number of variables, the number of alternative vine decompositions is very large and additionally there is a large set of candidate bivariate copula families that can be used as building blocks in any given decomposition. Together, these two issues ensure that it is infeasible to evaluate all possible vine copula models. Instead, two greedy algorithms for automatically selecting vine structures and component pair-copula building blocks are introduced. The algorithms are tested in a simulation study that is itself driven by real world data from online retail. Both algorithms select vines that provide accurate estimates of the joint probabilities. Using three different f-divergences as criteria, the proposed algorithms outperform a Gaussian copula benchmark, especially for data with high dependence. Finally, the selection algorithm is applied to data from the General Social Survey and outperforms a Gaussian copula benchmark using both in-sample and out-of-sample criteria. [ABSTRACT FROM AUTHOR]
Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 119156218
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Model selection for discrete regular vine copulas.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Panagiotelis%2C+Anastasios%22">Panagiotelis, Anastasios</searchLink><relatesTo>1</relatesTo><i> anastasios.panagiotelis@monash.edu</i><br /><searchLink fieldCode="AR" term="%22Czado%2C+Claudia%22">Czado, Claudia</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Joe%2C+Harry%22">Joe, Harry</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Stöber%2C+Jakob%22">Stöber, Jakob</searchLink><relatesTo>4</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computational+Statistics+%26+Data+Analysis%22">Computational Statistics & Data Analysis</searchLink>. Feb2017, Vol. 106, p138-152. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Copula+functions%22">Copula functions</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+systems%22">Discrete systems</searchLink><br /><searchLink fieldCode="DE" term="%22High-dimensional+model+representation%22">High-dimensional model representation</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Marginal+distributions%22">Marginal distributions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Discrete vine copulas provide a flexible modeling framework for high-dimensional data and have significant computational advantages over competing methods. A vine-based multivariate probability mass function is constructed from bivariate copula building blocks and univariate marginal distributions. However, even for a moderate number of variables, the number of alternative vine decompositions is very large and additionally there is a large set of candidate bivariate copula families that can be used as building blocks in any given decomposition. Together, these two issues ensure that it is infeasible to evaluate all possible vine copula models. Instead, two greedy algorithms for automatically selecting vine structures and component pair-copula building blocks are introduced. The algorithms are tested in a simulation study that is itself driven by real world data from online retail. Both algorithms select vines that provide accurate estimates of the joint probabilities. Using three different f-divergences as criteria, the proposed algorithms outperform a Gaussian copula benchmark, especially for data with high dependence. Finally, the selection algorithm is applied to data from the General Social Survey and outperforms a Gaussian copula benchmark using both in-sample and out-of-sample criteria. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=119156218
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.csda.2016.09.007
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 138
    Subjects:
      – SubjectFull: Copula functions
        Type: general
      – SubjectFull: Discrete systems
        Type: general
      – SubjectFull: High-dimensional model representation
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Marginal distributions
        Type: general
    Titles:
      – TitleFull: Model selection for discrete regular vine copulas.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Panagiotelis, Anastasios
      – PersonEntity:
          Name:
            NameFull: Czado, Claudia
      – PersonEntity:
          Name:
            NameFull: Joe, Harry
      – PersonEntity:
          Name:
            NameFull: Stöber, Jakob
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2017
              Type: published
              Y: 2017
          Identifiers:
            – Type: issn-print
              Value: 01679473
          Numbering:
            – Type: volume
              Value: 106
          Titles:
            – TitleFull: Computational Statistics & Data Analysis
              Type: main
ResultId 1