Dependence measure for length-biased survival data using copulas

Bentoumi Rachid, Mesfioui Mhamed () and Alvo Mayer
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Bentoumi Rachid: Department of Mathematics and Statistics, Zayed University, P.O. Box 144534, Abu Dhabi, United Arab Emirates
Mesfioui Mhamed: Département de mathématiques et d’informatique, Université du Québec à Trois-Rivières, C.P. 500, Trois-Rivières (Québec) Canada G9A 5H7
Alvo Mayer: Department of Mathematics and Statistics, University of Ottawa, STEM Complex, room 336, 150 Louis-Pasteur Pvt Ottawa, ON, Canada K1N 6N5

Dependence Modeling, 2019, vol. 7, issue 1, 348-364

Abstract: 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.

Keywords: Length-biased sampling; covariate distribution; length-biased distribution; information gain; dependence measure; kernel density estimation; copulas (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:7:y:2019:i:1:p:348-364:n:18

DOI: 10.1515/demo-2019-0018

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