Table of Contents
ISRN Probability and Statistics
Volume 2012 (2012), Article ID 717839, 7 pages
Research Article

On the Relationship between Pearson Correlation Coefficient and Kendall’s Tau under Bivariate Homogeneous Shock Model

Department of Mathematics, Kean University, Union, NJ 07083, USA

Received 2 March 2012; Accepted 12 April 2012

Academic Editors: M. Galea, J. Hu, and A. Pascucci

Copyright © 2012 Jiantian Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper studies the relationship between Kendall's tau and Pearson correlation coefficient under the so-called bivariate homogeneous shock (BHS) model. We find Capéraà-Genest-type inequality may not hold for general BHS model. Computational simulations suggest that the Denials' inequality is likely to be true.

1. Introduction

Pearson's correlation coefficient 𝑟 is the mostly used nonparametric measure of association for two random variables. Besides it, the Spearman's 𝜌 and Kendall's 𝜏 are two very useful measures of association. As we all know, the Spearman's 𝜌 is the ordinary Pearson's correlation coefficient.

The relationship between 𝜌 and 𝜏 has received considerable attention recently. For instance, Hutchinson and Lai [1] conjecture that 1+1+3𝜏𝜌min{3𝜏/2,2𝜏𝜏2} for stochastically increasing random variables. Hürlimann [2] has shown that the entire Hutchinson and Lai conjecture holds for bivariate extreme value distributions. Munroe et al. [3] show that the Hutchinson and Lai conjecture, 1+3𝜏(1+𝜌)2, does not hold when 𝑋 and 𝑌 are stochastically increasing. Fredricks and Nelsen [4] show that, under mild regularity conditions, the limit of the ratio 𝜌/𝜏 is 3/2 as the joint distribution of the random variables approaches to independence. Capéraà and Genest [5] have shown that 𝜌𝜏0 when one variable is simultaneously left-tail decreasing and the other right-tail increasing. Genest and Nešlehová [6] give a short analytical proof for classical Daniels' inequality |3𝜏2𝜌|1, found by Daniels [7].

However, relatively little attention has been paid on the relationships between 𝑟 and 𝜏. One example on this track is Edwardes [8], in which the author has shown that 𝑟=𝜏 under a bivariate exponential (BVE) model introduced by Marshall and Olkin [9].

Based on a two-component series system subjected to some fatal shocks, Wang and Li [10] introduced a more general bivariate model, which is referred to as bivariate homogeneous shock (BHS) model. In this paper, we study the relationships between 𝑟 and 𝜏 under BHS model. We find that the most revealed relationships between 𝜌 and 𝜏 will no longer hold for the relationships between 𝑟 and 𝜏. However, computational simulations suggest that the Daniels-type inequality, |3𝜏2𝑟|1, is likely to be true.

2. Main Results

For any pair of random variables (𝑋,𝑌), let 𝐹(𝑥,𝑦) be its bivariate cumulative distribution function. The classical Pearson correlation coefficient 𝑟 of (𝑋,𝑌) is defined as follows: 𝑟=Cov(𝑋,𝑌){Var(𝑋)Var(𝑌)}1/2,(2.1) and Kendall's 𝜏 is defined as follows: 𝜏=4𝐹(𝑥,𝑦)𝑑𝐹(𝑥,𝑦)1.(2.2)

Consider a two-component series system subjected to some fatal shocks. Assume there are three kinds of fatal shocks. Shock A governed by random variable 𝑈 destroys component 1, shock B governed by random variable 𝑉 destroys component 2, and shock C governed by random variable 𝑊 destroys both components simultaneously. We refer to such a system as bivariate homogeneous shock (BHS) model. Clearly, under this model the life length of component 1 is 𝑋=min(𝑈,𝑊) and that of component 2 is 𝑌=min(𝑉,𝑊). Especially, if the random variables 𝑈, 𝑉, and 𝑊 are all exponential, the BHS model is just the BVE model proposed by Marshall and Olkin [9].

A prominent feature of BHS model is its singularity. More specifically, even though 𝑈, 𝑉, and 𝑊 all are continuous random variables, the joint distribution of 𝑋 and 𝑌 is usually discontinuous.

Denote the survival functions of 𝑈,𝑉, and 𝑊 as 𝑢(𝑥)=pr(𝑈>𝑥), 𝑣(𝑥)=pr(𝑉>𝑥), and 𝑤(𝑥)=pr(𝑊>𝑥), respectively. Wang and Li [10] have shown that under BHS model, 𝑢𝜏=2𝑣2𝑑𝑤2,(2.3)𝑟=𝑢(𝑥)𝑣(𝑦)𝑤(𝑥𝑦)𝑑𝑥𝑑𝑦(𝑢𝑤)(𝑥𝑦)𝑑𝑥𝑑𝑦1/2(𝑣𝑤)(𝑥𝑦)𝑑𝑥𝑑𝑦1/2,(2.4) where 𝑓(𝑥𝑦) is defined as 𝑓(𝑥𝑦)=𝑓(𝑥𝑦)𝑓(𝑥)𝑓(𝑦) for any function 𝑓(𝑥) with the maximization operator.

In the same manner as Chen et al. [11], we define a proportional hazard model as a submodel of BHS. We say a BHS model is a proportional hazards model if there exist some constants 𝜆, 𝛼, and 𝛽, such that 𝑢(𝑥)=𝑢𝜆0(𝑥),𝑣(𝑥)=𝑢𝛼0(𝑥),𝑤(𝑥)=𝑢𝛽0(𝑥),(2.5) for some baseline survival function 𝑢0(𝑥). Clearly, the BVE model is a proportional hazard model with 𝑢0(𝑥)=𝑒𝑥,𝑥0.

We refer the constants 𝜆, 𝛼, and 𝛽 as to shape constants since they are determined by the structural representation of system. If an association measure depends only on the structural representation of system, we then say it is fully structure determined.

Under proportional hazard model, by (2.3), we can easily obtain 𝛽𝜏=.(𝜆+𝛼+𝛽)(2.6) Thus, the Kendall's 𝜏 is fully structure determined. The correlation coefficient 𝑟, as we can verify, is not fully structure determined.

The exact expression of 𝑟 is not so easy to obtain in general, except for two special cases when 𝑢0(𝑥)=𝑒𝑥,𝑥0, or 𝑢0(𝑥)=1𝑥,0𝑥1, that is, when the baseline distribution is exponential or uniform. For our convenience, we refer to the first model as model A and the second one as model B. Clearly, model A is just the BVE model. Now we list the main results in the following theorem.

Theorem 2.1. Let 𝑟 and 𝜏 be the Pearson's correlation coefficient and Kendall's tau. Then under models A or B, one has the following.(i)𝑟𝜏.(ii)The limit of the ratio of 𝑟/𝜏 can be any number larger than 1 when the model approaches to independence situation.(iii)|2𝑟3𝜏|1.

Proof. The results of Theorem 2.1 in model A are just that of Edwardes [8]. So, we only need to focus on model B. Denote 𝑢𝑔(𝜃)=𝜃(𝑥𝑦)𝑑𝑥𝑑𝑦, and 𝑢𝑓(𝜆,𝛼,𝛽)=𝜆(𝑥)𝑢𝛼(𝑦)𝑢𝛽(𝑥𝑦)𝑑𝑥𝑑𝑦. When 𝑢(𝑥)=1𝑥,0𝑥1, we have, 𝑢𝑔(𝜃)=𝜃(𝑥𝑦)𝑑𝑥𝑑𝑦=210(1𝑥)𝜃𝑑𝑥𝑥01(1𝑦)𝜃𝜃𝑑𝑦=(𝜃+1)2.𝑢(𝜃+2)(2.7)𝑓(𝜆,𝛼,𝛽)=𝜆(𝑥)𝑢𝛼(𝑦)𝑢𝛽=(𝑥𝑦)𝑑𝑥𝑑𝑦𝑥𝑦𝑢𝜆(𝑥)𝑢𝛼(𝑦)𝑢𝛽(𝑥)1𝑢𝛽+(𝑦)𝑑𝑥𝑑𝑦𝑥<𝑦𝑢𝜆(𝑥)𝑢𝛼(𝑦)𝑢𝛽(𝑦)1𝑢𝛽=1(𝑥)𝑑𝑥𝑑𝑦1𝛼+11𝜆+𝛽+11𝛼+11𝜆+𝛼+𝛽+21𝜆+𝛽+1+1𝛼+𝛽+11𝛼+𝛽+1+1𝜆+𝛼+2𝛽+21𝜆+11𝛼+𝛽+11𝜆+11𝜆+𝛼+𝛽+21𝜆+𝛽+1+1𝛼+𝛽+11𝜆+𝛽+1=𝛽𝜆+𝛼+2𝛽+2.(𝛼+𝛽+1)(𝜆+𝛽+1)(𝜆+𝛼+𝛽+2)(2.8) Thus, we obtain, 𝛽𝑟=(𝜆+𝛼+𝛽+2)(𝜆+𝛽+2)(𝛼+𝛽+2).(𝜆+𝛽)(𝛼+𝛽)(2.9)

Denote 𝑟Γ=𝜏=𝜆+𝛼+𝛽𝜆+𝛼+𝛽+2(𝜆+𝛽+2)(𝛼+𝛽+2).(𝜆+𝛽)(𝛼+𝛽)(2.10)

We want to show Γ>1. Denote 𝛼+𝛽=𝑢, 𝜆+𝛽=𝑣, and 𝜆+𝛼+𝛽=𝑤. Then, 𝑟Γ=𝜏=𝑤𝑤+2𝑢+2𝑢𝑣+2𝑣=11+(2/𝑤)21+𝑢21+𝑣.(2.11)

Clearly, we have, max{𝑢,𝑣}𝑤𝑢+𝑣. With a little bit notational confusion, we relabel 2/𝑢, 2/𝑣 and 2/𝑤 as 𝑢, 𝑣, and 𝑤, respectively. Then, we have, 𝑤min{𝑢,𝑣}. Without loss of generality, we assume 𝑢𝑣, and then, 1Γ=1+𝑤1(1+𝑢)(1+𝑣)1+𝑣(1+𝑣)(1+𝑣)=1.(2.12)

As we can see, the equality holds only when 𝑢=𝑣=𝑤, that is, 𝛼=𝜆=𝛽=0. Hence, when the three parameters are not all zero, Γ>1, and thus 𝑟>𝜏.

Consider Φ=lim𝛽0+𝑟𝜏=𝜆+𝛼𝜆+𝛼+221+𝜆21+𝛼.(2.13)

In a similar way, we can show that Φ>1. We can show that Φ can be any number that is larger than 1. Let 𝛼=𝑘𝜆, then, Φ=(1+𝜆)(1+𝑘𝜆).1+(𝑘/(1+𝑘))𝜆(2.14) As 𝑘0, Φ1+𝜆, which can be any number that is larger than 1.

Denote Ψ=2𝑟3𝜏, then, Ψ=2𝛽(𝜆+𝛼+𝛽+2)(𝜆+𝛽+2)(𝛼+𝛽+2)(𝜆+𝛽)(𝛼+𝛽)3𝛽.𝜆+𝛼+𝛽(2.15) Since Ψ is symmetric about 𝛼 and 𝜆, the minimum or maximum of Ψ will be attained on 𝛼=𝜆. So we just need to show that the minimum or maximum of Ψ will be between 1 and 1.

When 𝛼=𝜆, Ψ becomes Ψ=2𝛽(2𝛼+𝛽+2)𝛼+𝛽+2𝛼+𝛽3𝛽=2𝛼+𝛽2𝛽(𝛼+𝛽+2)(2𝛼+𝛽)3𝛽(2𝛼+𝛽+2)(𝛼+𝛽)=𝑁(𝛼+𝛽)(2𝛼+𝛽)(2𝛼+𝛽+2)𝐷,(2.16) where 𝛽𝑁=3+𝛽2(3𝛼+2)+𝛽2𝛼2,2𝛼𝐷=𝛽3+𝛽2(5𝛼+2)+𝛽8𝛼2+6𝛼+4𝛼3+4𝛼.(2.17)

We have, 𝐷+𝑁=2𝛼𝛽2+6𝛼2+8𝛼𝛽+4𝛼2(𝛼+1)>0,𝐷𝑁=2𝛽3+(8𝛼+4)𝛽2+10𝛼2+4𝛼𝛽+4𝛼2(𝛼+1)>0.(2.18)

Hence, Ψ=𝑁/𝐷 will be between 1 and 1.

3. Computational Simulations

In order to investigate the relationships between 𝑟 and 𝜏 under general BHS model, we conduct some computational simulations. For the sample data {(𝑥𝑖,𝑦𝑖),𝑖=1,2,,𝑛}, the sample's 𝑟 is computed as follows: 𝑥̂𝑟=𝑖𝑥𝑦𝑖𝑦𝑥𝑖𝑥2𝑦𝑖𝑦2.(3.1) While the sample's Kendall's 𝜏 is computed as follows: 𝑛2̂𝜏=11𝑖<𝑗𝑛𝑎𝑖𝑗𝑏𝑖𝑗,(3.2) where 𝑎𝑖𝑗=1 if 𝑥𝑖<𝑥𝑗, and 1 if 𝑥𝑖>𝑥𝑗, and 𝑏𝑖𝑗=1 if 𝑦𝑖<𝑦𝑗, and 1 if 𝑦𝑖>𝑦𝑗. We compute the sample values of 𝑟, 𝜏, 𝑟/𝜏, and 2𝑟3𝜏 under several cases. The sample size is set as 𝑛=100, and for each computation, the iteration is 100. Table 1 gives the results.

Table 1

In case 1, we set 𝑈, 𝑉, and 𝑊 as uniform variables on [0,1]. By (2.6) and (2.9), we obtain, 𝑟=0.4 and 𝜏=1/3, and the ratio 𝑟/𝜏=1.2. In case 2, the variables 𝑈, 𝑉, and 𝑊 all follow exponential distributions with means 1,2, and 3, respectively. In this case, 𝑟=𝜏=(1/3)/(1+1/2+1/3)=2/11=0.1818, so the ratio is exactly 1. Based on the computation results for these two cases, we can see that the numerical computations are quite precise. In case 3, we set all variables to follow standard normal distribution. In case 4, we set 𝑈=𝑍2, 𝑉=(𝑍+0.2)2, and 𝑊=(𝑍+1.0)2, where 𝑍 is the variable of standard normal. In case 5, we set 𝑈=𝐸21, 𝑉=𝐸22, and 𝑊=𝐸23, where 𝐸1 exp(1), 𝐸2 exp(2), and 𝐸3 exp(3). Surprisingly, in this case, the ratio 𝑟/𝜏 is less than 1. In all the cases, we find the Daniels' inequality |2𝑟3𝜏|1 holds.

4. Concluding Remarks

The relationship between Spearman's 𝜌, which is the ordinary Pearson correlation coefficient, and Kendall's 𝜏 has been of interest for a long time. However, little attention has been paid on the relationship of Pearson correlation coefficient and Kendall's 𝜏. In this paper, we investigate their relationship under the so-called BHS model. We find that even though for some typical BHS models, the Capéraà-Genest type inequality, 𝑟𝜏0, holds, but for general BHS model, the inequality may not hold. Our simulation studies suggest that the Daniels-type inequality, 12𝑟3𝜏1, will hold under BHS model. We thus conjecture that the Daniels-type inequality will be valid in general. However, theoretical confirmation for such conjecture merits further study.


  1. T. P. Hutchinson and C. D. Lai, Continuous Multivariate Distributions, Emphasising Applications, Rumsby Scientific Publishing, Adelaide, Australia, 1990.
  2. W. Hürlimann, “Hutchinson-Lai's conjecture for bivariate extreme value copulas,” Statistics & Probability Letters, vol. 61, no. 2, pp. 191–198, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  3. P. Munroe, T. Ransford, and C. Genest, “Un contre-exemple à une conjecture de Hutchinson et Lai,” Les Comptes Rendus de l'Académie des Sciences de Paris, Series I, vol. 348, no. 5-6, pp. 305–310, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  4. G. A. Fredricks and R. B. Nelsen, “On the relationship between Spearman's rho and Kendall's tau for pairs of continuous random variables,” Journal of Statistical Planning and Inference, vol. 137, no. 7, pp. 2143–2150, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  5. P. Capéraà and C. Genest, “Spearman's ρ is larger than Kendall's τ for positively dependent random variables,” Journal of Nonparametric Statistics, vol. 2, no. 2, pp. 183–194, 1993. View at Publisher · View at Google Scholar
  6. C. Genest and J. Nešlehová, “Analytical proofs of classical inequalities between Spearman's ρ and Kendall's τ,” Journal of Statistical Planning and Inference, vol. 139, no. 11, pp. 3795–3798, 2009. View at Publisher · View at Google Scholar
  7. H. E. Daniels, “Rank correlation and population models,” Journal of the Royal Statistical Society Series B, vol. 12, pp. 171–181, 1950. View at Google Scholar · View at Zentralblatt MATH
  8. M. D. Edwardes, “Kendall's τ is equal to the correlation coefficient for the BVE distribution,” Statistics & Probability Letters, vol. 17, no. 5, pp. 415–419, 1993. View at Publisher · View at Google Scholar
  9. A. W. Marshall and I. Olkin, “A multivariate exponential distribution,” Journal of the American Statistical Association, vol. 62, pp. 30–44, 1967. View at Google Scholar · View at Zentralblatt MATH
  10. J. Wang and Y. Li, “Dependency measures under bivariate homogeneous shock models,” Statistics, vol. 39, no. 1, pp. 73–80, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  11. Y. Y. Chen, M. Hollander, and N. A. Langberg, “Small-sample results for the Kaplan-Meier estimator,” Journal of the American Statistical Association, vol. 77, no. 377, pp. 141–144, 1982. View at Google Scholar · View at Zentralblatt MATH