Abstract
Measuring the correlation between two random variables is an important goal in various statistical applications. The standardized covariance is a widely used criterion for measuring the linear association. In this paper, first, we propose a covariance-based unified measure of variability for a continuous random variable X and show that several measures of variability and uncertainty, such as variance, Gini mean difference and cumulative residual entropy arise as special cases. Then, we propose a unified measure of correlation between two continuous random variables X and Y, with distribution functions (DFs) F and G. Assuming that H is a continuous DF, the proposed measure is defined based on the covariance between X and the transformed random variable \(H^{-1}G(Y)\) (known as the Q-transformation of H on G). We show that our proposed measure of association subsumes some of the existing measures of correlation. Under some mild condition on H, it is shown that the suggested index ranges in \([-1,1]\) where the extremes of the range, i.e., \(-1\) and 1, are attainable by the Fréchet bivariate minimal and maximal DFs, respectively. A special case of the proposed correlation measure leads to a variant of the Pearson correlation coefficient which has absolute values greater than or equal to Pearson correlation. The results are examined numerically for some well known bivariate DFs.
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References
Aly EEA, Bleuer S (1986) Confidence bands for quantile–quantile plots. Stat Risk Model 4(2–3):205–226
Arnold BC, Balakrishnan N, Nagaraja HN (1998) Records. Wiley, New York
Asadi M (2017) A new measure of association between random variables. Metrika 80(6–8):649–661
Asadi M, Zohrevand Y (2007) On the dynamic cumulative residual entropy. J Stat Plan Inference 137(6):1931–1941
Asadi M, Ebrahimi N, Soofi ES (2017) Connections of Gini, Fisher, and Shannon by Bayes risk under proportional hazards. J Appl Probab 54(4):1027–1050
Balanda KP, MacGillivray HL (1990) Kurtosis and spread. Can J Stat 18(1):17–30
Barlow RE, Proschan F (1981) Statistical theory of reliability and life testing: probability models. To begin with. Springer, Berlin
Barnett V (1980) Some bivariate uniform distributions. Commun Stat Theory Methods 9:453–461
Cambanis S (1991) On Eyraud–Farlie–Gumbel–Morgenstern random processes. In: Salinetti G, Kotz S (eds) Advances in probability distributions with given marginals. Springer, Netherlands, pp 207–222
Cuadras CM (2002) On the covariance between functions. J Multivar Anal 81:19–27
Diaz W, Cuadras CM (2017) On a multivariate generalization of the covariance. Commun Stat Theory Methods 46(9):4660–4669
Doksum KA (1975) Measures of location and asymmetry. Scand J Stat 12:11–22
Doksum KA, Fenstad G, Aaberge R (1977) Plots and tests for symmetry. Biometrika 64(3):473–487
Furman E, Zitikis R (2017) Beyond the Pearson correlation: heavytailed risks, weighted Gini correlations, and a Gini-type weighted insurance pricing model. ASTIN Bull J IAA 47(3):919–942
Gilchrist W (2000) Statistical modelling with quantile functions. CRC Press Inc, Boca Raton
Groeneveld RA (1998) A class of quantile measures for kurtosis. Am Stat 52(4):325–329
Grothe O, Schnieders J, Segers J (2014) Measuring association and dependence between random vectors. J Multivar Anal 123:96–110
Gurrera MDC (2005) Construction of bivariate distributions and statistical dependence operations. Ph.D. dissertation, University of Barcelona, Spain
Hutchinson TP, Lai CD (1990) Continuous bivariate distributions, emphasising applications. Rumsby Scientific Publishing, Adelaide
Johnson NL, Kotz S (1977) On some generalized Farlie–Gumbel–Morgenstern distributions-II regression, correlation and further generalizations. Commun Stat Theory Methods 6(6):485–496
Nelsen RB (1998) Concordance and Gini’s measure of association. Nonparametric Stat 9:227–238
Nolde N (2014) Geometric interpretation of the residual dependence coefficient. J Multivar Anal 123:85–95
Psarrakos G, Navarro J (2013) Generalized cumulative residual entropy and record values. Metrika 27:623–640
Rao M, Chen Y, Vemuri BC, Wang F (2004) Cumulative residual entropy: a new measure of information. IEEE Trans Inf Theory 50(6):1220–1228
Schechtman E, Yitzhaki S (1999) On the proper bounds of the Gini correlation. Econ lett 63(2):133–138
Schezhtman E, Yitzhaki S (1987) A measure of association based on Gini’s mean difference. Commun Stat Theory Methods 16(1):207–231
Shaked M, Shanthikumar JG (2007) Stochastic orders. Springer, Berlin
Shaw WT, Buckley IR (2009) The alchemy of probability distributions: beyond Gram-Charlier expansions, and a skew-kurtotic-normal distribution from a rank transmutation map. arXiv preprint arXiv:0901.0434
van Zwet WR (1964) Convex T ransformations of random variables. Mathematisch Centrum, Amsterdam
Wang Y, Hossain AM, Zimmer WJ (2003) Monotone log-odds rate reliability analysis. Commun Stat Theory Methods 12:2227–2244
Yeo IK, Johnson RA (2000) A new family of power transformations to improve normality or symmetry. Biometrika 87(4):954–959
Yin X (2004) Canonical correlation analysis based on information theory. J Multivar Anal 91(2):161–176
Yitzhaki S (2003) Gini’s mean difference: a superior measure of variability for non-normal distributions. Metron 61(2):285–316
Yitzhaki S, Olkin I (1991) Concentration indices and concentration curves. Lecture notes-monograph series. Stanford University, Stanford, pp 380–392
Yitzhaki S, Schechtman E (2013) The Gini methodology: a primer on a statistical methodology. Springer, New York
Yitzhaki S, Wodon Q (2004) Mobility, inequality, and horizontal equity. In: Amiel Y (ed) Studies on economic well-being: essays in the honor of John P. Formby. Emerald Group Publishing Limited, Bingley, pp 179–199
Acknowledgements
The authors would like to thank the editor Professor Holzmann, Associate Editor and two anonymous reviewers for thoughtful comments and suggestions which improved the exposition of the article. In particular, representation (19) was proposed by one of the reviewers, for which the authors are thankful. Asadi’s research work was performed in IPM Isfahan branch and was in part supported by a grant from IPM (No. 98620215).
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Asadi, M., Zarezadeh, S. A unified approach to constructing correlation coefficients between random variables. Metrika 83, 657–676 (2020). https://doi.org/10.1007/s00184-019-00759-w
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DOI: https://doi.org/10.1007/s00184-019-00759-w