Nonparametric estimation of pair-copula constructions with the empirical pair-copula

DOI10.1016/j.csda.2014.10.020OpenAlexW2166486650MaRDI QIDQ1623802

Johan Segers, Ingrid Hobæk Haff

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1201.5133

zbMATH Keywords

independencesmoothingmodel selectionresamplingempirical copulapair-copularegular vineSpearman rank correlation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Asymptotic properties of nonparametric inference (62G20) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Order statistics; empirical distribution functions (62G30)

Related Items (11)

Nonparametric estimation of simplified vine copula models: comparison of methods ⋮ Default probability estimation via pair copula constructions ⋮ Smooth nonparametric Bernstein vine copulas ⋮ Structure learning in Bayesian networks using regular vines ⋮ Unnamed Item ⋮ Vine copula approximation: a generic method for coping with conditional dependence ⋮ On the weak convergence of the empirical conditional copula under a simplifying assumption ⋮ Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas ⋮ Derivatives and Fisher information of bivariate copulas ⋮ Selection of Vine Copulas ⋮ Conditional independence testing via weighted partial copulas

Uses Software

• CDVine
• TwoCop

Cites Work

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• Pair-copula constructions of multiple dependence
• Selecting and estimating regular vine copulae and application to financial returns
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• Uncertainty Analysis with High Dimensional Dependence Modelling
• A NONPARAMETRIC HELLINGER METRIC TEST FOR CONDITIONAL INDEPENDENCE
• A sharper Bonferroni procedure for multiple tests of significance
• A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
• Empirical processes indexed by estimated functions
• Semiparametric estimation in copula models

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