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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/18220
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/18220


    Title: Nonparametric estimation of copula functions for dependence modelling
    Authors: S. X. Chen;黃子銘
    Date: 2007-06
    Issue Date: 2008-12-19 14:56:29 (UTC+8)
    Abstract: Copulas characterize the dependence among components of random vectors. Unlike marginal and joint distributions, which are directly observable, the copula of a random vector is a hidden dependence structure that links the joint distribution with its margins. Choosing a parametric copula model is thus a nontrivial task but it can be facilitated by relying on a nonparametric estimator. Here the authors propose a kernel estimator of the copula that is mean square consistent everywhere on the support. They determine the bias and variance of this estimator. They also study the effects of kernel smoothing on copula estimation. They then propose a smoothing bandwidth selection rule based on the derived bias and variance. After confirming their theoretical findings through simulations, they use their kernel estimator to formulate a goodness-of-fit test for parametric copula models.Estimation non paramétrique de copules à des fins de modélisation de la dépendanceLes copules caractérisent la dépendance entre les composantes de vecteurs aléatoires. À l`inverse de la loi jointe et des marges, qui sont directement observables, la copule d`un vecteur aléatoire est une structure de dépendance cachée qui lie la loi multivariée et les marges. Le choix d`un modèle de copules paramétrique n`est donc pas chose aisée, mais il peut ětre facilité grǎce à un estimateur non paramétrique. Les auteurs proposent ici un estimateur à noyau de la copule qui est convergent en moyenne quadratique sur le support. Ils déterminent le biais et la variance de cet estimateur. Ils étudient aussi les effets d`un lissage par noyau sur l`estimation de la copule. Ils proposent ensuite une façon de choisir le paramètre de lissage qui s`appuie sur leurs calculs de biais et de variance. Après avoir confirmé leurs résultats théoriques au moyen de simulations, ils se servent de leur estimateur à noyau pour construire un test d`adéquation d`un modèle de copules paramétrique.
    Relation: The Canadian Journal of Statistics, 35(2),265-282
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1002/cjs.5550350205
    DOI: 10.1002/cjs.5550350205
    Appears in Collections:[統計學系] 期刊論文

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