Dependence measure for length-biased survival data using copulas

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...

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Bibliographic Details
Published in:Dependence modeling 2019-01, Vol.7 (1), p.348-364
Main Authors: Bentoumi, Rachid, Mesfioui, Mhamed, Alvo, Mayer
Format: Article
Language:English
Subjects:
60H20
62F12
62F40
62G07
62H05
62H12
copulas
covariate distribution
dependence measure
information gain
kernel density estimation
length-biased distribution
Length-biased sampling
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Summary: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
2300-2298
DOI:10.1515/demo-2019-0018