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Volume 5 • Issue 2 | July 2021

On subcopula estimation for discrete models

Santi Tasena

01/11/2021 68 0 0
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Copula Discrete model Empirical subcopula Subcopula

Abstract:

Purpose – To discuss subcopula estimation for discrete models.
Design/methodology/approach – The convergence of estimators is considered under the weak convergence of distribution functions and its equivalent properties known in prior works.
Findings – The 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/value – To the best of our knowledge, this is the first time such an estimator is defined and proved to be converged to the true subcopula.

References:

  1. Boonmee, T. and Tasena, S. (2016), “Measure of complete dependence of random vectors”, Journal of Mathematical Analysis and Applications, Vol. 443 No. 1, pp. 585-595.
  2. De Amo, E., Carrillo, M.D., Durante, F. and Fernández-Sánchez, J. (2017), “Extensions of subcopulas”, Journal of Mathematical Analysis and Applications, Vol. 452 No. 1, pp. 1-15.
  3. Dette, H., Siburg, K.F. and Stoimenov, P.A. (2013), “A copula-based non-parametric measure of regression dependence”, Scandinavian Journal of Statistics, Vol. 40 No. 1, pp. 21-41.
  4. Faugeras, O.P. (2017), “Inference for copula modeling of discrete data: a cautionary tale and some facts”, Dependence Modeling, Vol. 5 No. 1, pp. 121-132.
  5. Geenens, G. (2020), “Copula modeling for discrete random vectors”, Dependence Modeling, Vol. 8 No. 1, pp. 417-440.
  6. Nikoloulopoulos, A.K. (2013), “A survey on multivariate copula-based models for multivariate discrete response data”, Copulae in Mathematical and Quantitative Finance, Springer, pp. 231-249.
  7. Rachasingho, J. and Tasena, S. (2018), “Metric space of subcopulas”, Thai Journal of Mathematics, pp. 35-44.
  8. Rachasingho, J. and Tasena, S. (2020), “A metric space of subcopulas - an approach via hausdorff distance”, Fuzzy Sets and Systems, Vol. 378, pp. 144-156.
  9. Siburg, K. and Stoimenov, P. (2010), “A measure of mutual complete dependence”, Metrika, Vol. 71 No. 2, pp. 239-251.
  10. Tasena, S. (2020), “Complete dependence”, Direction Dependence in Statistical Modeling: Methods of Analysis, p. 167.
  11. Tasena, S. (2021a), “On a distribution form of subcopulas”, International Journal of Approximate Reasoning, Vol. 128, pp. 1-19.
  12. Tasena, S. (2021b), “On metric spaces of subcopulas”, Fuzzy Sets and Systems, Vol. 415, pp. 76-88.
  13. Tasena, S. and Dhompongsa, S. (2013), “A measure of multivariate mutual complete dependence”, International Journal of Approximate Reasoning, Vol. 54 No. 6, pp. 748-761.
  14. Tasena, S. and Dhompongsa, S. (2016), “Measures of the functional dependence of random vectors”, International Journal of Approximate Reasoning, Vol. 68, pp. 15-26.
  15. Trivedi, P. and Zimmer, D. (2017), “A note on identification of bivariate copulas for discrete count data”, Econometrics, Vol. 5 No. 1, p. 10.

JEL classification: C13,C18,C46

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