EM algorithms for estimating the Bernstein copula
Computation
2014-01-16 v4
Abstract
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.
Cite
@article{arxiv.1301.2677,
title = {EM algorithms for estimating the Bernstein copula},
author = {Xiaoling Dou and Satoshi Kuriki and Gwo Dong Lin and Donald Richards},
journal= {arXiv preprint arXiv:1301.2677},
year = {2014}
}
Comments
34 pages, 7 figures, 3 tables