EM algorithms for estimating the Bernstein copula

Xiaoling Dou*, Satoshi Kuriki, Gwo Dong Lin, Donald Richards

*この研究の対応する著者

研究成果: Article査読

10 被引用数 (Scopus)

抄録

A method that uses order statistics to construct multivariate distributions with fixed marginals and which utilizes a representation of the Bernstein copula in terms of a finite mixture distribution is proposed. Expectation-maximization (EM) algorithms to estimate the Bernstein copula are proposed, and a local convergence property is proved. Moreover, asymptotic properties of the proposed semiparametric estimators are provided. Illustrative examples are presented using three real data sets and a 3-dimensional simulated data set. These studies show that the Bernstein copula is able to represent various distributions flexibly and that the proposed EM algorithms work well for such data.

本文言語English
ページ(範囲)228-245
ページ数18
ジャーナルComputational Statistics and Data Analysis
93
DOI
出版ステータスPublished - 2016 1 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 計算数学
  • 計算理論と計算数学
  • 応用数学

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