Document Type

Article

Source of Publication

Dependence Modeling

Publication Date

1-1-2019

Abstract

© 2019 Rachid Bentoumi et al., published by De Gruyter 2019. The linear correlation coefficient of Bravais-Pearson is considered a powerful indicator when the dependency relationship is linear and the error variate is normally distributed. Unfortunately in finance and in survival analysis the dependency relationship may not be linear. In such case, the use of rank-based measures of dependence, like Kendall's tau or Spearman rho are recommended. In this direction, under length-biased sampling, measures of the degree of dependence between the survival time and the covariates appear to have not received much intention in the literature. Our goal in this paper, is to provide an alternative indicator of dependence measure, based on the concept of information gain, using the parametric copulas. In particular, the extension of the Kent's [18] dependence measure to length-biased survival data is proposed. The performance of the proposed method is demonstrated through simulations studies.

ISSN

2300-2298

Publisher

De Gruyter Open Ltd

Volume

7

Issue

1

First Page

348

Last Page

364

Disciplines

Physical Sciences and Mathematics

Keywords

copulas, covariate distribution, dependence measure, information gain, kernel density estimation, length-biased distribution, Length-biased sampling

Scopus ID

85075482605

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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