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Dependence measure for length-biased survival data using copulas

Dependence measure for length-biased survival data using copulas AbstractThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

Dependence measure for length-biased survival data using copulas

Dependence Modeling , Volume 7 (1): 17 – Jan 1, 2019

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Publisher
de Gruyter
Copyright
© 2019 Rachid Bentoumi et al., published by De Gruyter
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2019-0018
Publisher site
See Article on Publisher Site

Abstract

AbstractThe 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.

Journal

Dependence Modelingde Gruyter

Published: Jan 1, 2019

References