On subcopula estimation for discrete models

Author:

Tasena Santi

Abstract

PurposeTo discuss subcopula estimation for discrete models.Design/methodology/approachThe convergence of estimators is considered under the weak convergence of distribution functions and its equivalent properties known in prior works.FindingsThe domain of the true subcopula associated with discrete random variables is found to be discrete on the interior of the unit hypercube. The construction of an estimator in which their domains have the same form as that of the true subcopula is provided, in case, the marginal distributions are binomial.Originality/valueTo the best of our knowledge, this is the first time such an estimator is defined and proved to be converged to the true subcopula.

Publisher

Emerald

Reference15 articles.

1. Measure of complete dependence of random vectors;Journal of Mathematical Analysis and Applications,2016

2. Extensions of subcopulas;Journal of Mathematical Analysis and Applications,2017

3. A copula-based non-parametric measure of regression dependence;Scandinavian Journal of Statistics,2013

4. Inference for copula modeling of discrete data: a cautionary tale and some facts;Dependence Modeling,2017

5. Copula modeling for discrete random vectors;Dependence Modeling,2020

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