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.

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dependency relationship the survival time parametric copulas kendalls tau finance lengthbiased survival data kents 18 dependence measure measures of dependence information error variate linear correlation coefficient of bravaispearson spearman rho covariates

Bentoumi Rachid,Mesfioui Mhamed,Alvo Mayer,.Dependence measure for length-biased survival data using copulas. 7 (1),.

Bentoumi Rachid,Mesfioui Mhamed,Alvo Mayer,"Dependence measure for length-biased survival data using copulas" 7

Bentoumi Rachid,Mesfioui Mhamed,Alvo Mayer,"Dependence measure for length-biased survival data using copulas"