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Volume 53, 2019 - Issue 2
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Original Articles

Multi-Panel Kendall plot in light of an ROC curve analysis applied to measuring dependence

, &
Pages 417-439 | Received 02 Aug 2017, Accepted 19 Nov 2018, Published online: 11 Dec 2018

References

  • Balakrishnan N, Lai C-D. Continuous bivariate distributions. 2nd ed. Dordrecht: Springer; 2009. DOI:10.1007/b101765
  • Reshef DN, Reshef YA, Finucane HK, et al. Detecting novel associations in large data sets. Science. 2011;334:1518–1524. doi: 10.1126/science.1205438
  • Vexler A, Tsai W-M, Hutson AD. A simple density-based empirical likelihood ratio test for independence. Amer Statist. 2014;68:158–169. DOI: 10.1080/00031305.2014.901922
  • Bjerve S, Doksum K. Correlation curves: measures of association as functions of covariate values. Ann Statist. 1993;21:890–902. doi: 10.1214/aos/1176349156
  • Fisher NI, Switzer P. Chi-plots for assessing dependence. Biometrika. 1985;72:253–265. doi: 10.1093/biomet/72.2.253
  • Fisher NI, Switzer P. Graphical assessment of dependence: is a picture worth 100 tests? Amer Statist. 2001;55:233–239. doi: 10.1198/000313001317098248
  • Jones MC. The local dependence function. Biometrika. 1996;83:899–904. doi: 10.1093/biomet/83.4.899
  • Jones MC, Koch I. Dependence maps: local dependence in practice. Stat Comput. 2003;13:241–255. doi: 10.1023/A:1024270700807
  • Gargouri-Ellouze E, Bargaoui Z. Investigation with Kendall plots of infiltration index–maximum rainfall intensity relationship for regionalization. Phys Chem Earth Parts A/B/C. 2009;34:642–653. doi: 10.1016/j.pce.2009.02.001
  • Genest C, Boies J-C. Detecting dependence with Kendall plots. Amer Statist. 2003;57:275–284. doi: 10.1198/0003130032431
  • Genest C, Rivest L-P. Statistical inference procedures for bivariate Archimedean copulas. J Amer Statist Assoc. 1993;88:1034–1043. doi: 10.1080/01621459.1993.10476372
  • Nelsen RB, Quesada-Molina JJ, Lallena Rodríguez JA, et al. Kendall distribution functions. Statist Probab Lett. 2003;65:263–268. DOI: 10.1016/j.spl.2003.08.002
  • Boero G, Silvapulle P, Tursunalieva A. Modelling the bivariate dependence structure of exchange rates before and after the introduction of the euro: a semi-parametric approach. Int J Finance Econ. 2011;16:357–374. doi: 10.1002/ijfe.434
  • Eslamian S. Handbook of engineering hydrology: fundamentals and applications. New York: CRC Press; 2014.
  • Shapiro DE. The interpretation of diagnostic tests. Stat Methods Med Res. 1999;8:113–134. doi: 10.1177/096228029900800203
  • Vexler A, Liu A, Eliseeva E, et al. Maximum likelihood ratio tests for comparing the discriminatory ability of biomarkers subject to limit of detection. Biometrics. 2008;64:895–903. DOI: 10.1111/j.1541-0420.2007.00941.x
  • Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J Math Psych. 1975;12:387–415. doi: 10.1016/0022-2496(75)90001-2
  • Breiman L, Friedman JH, Olshen RA, et al. Classification and regression trees. Belmont, CA: Wadsworth Advanced Books and Software; 1984. (Wadsworth Statistics/Probability Series).
  • Schriever BF. An ordering for positive dependence. Ann Statist. 1987;15:1208–1214. DOI: 10.1214/aos/1176350500
  • Schucany WR, Parr WC, Boyer JE. Correlation structure in Farlie-Gumbel-Morgenstern distributions. Biometrika. 1978;65:650–653. DOI: 10.1093/biomet/65.3.650
  • Rényi A. On measures of dependence. Acta Math Acad Sci Hungar. 1959;10:441–451. DOI: 10.1007/BF02024507
  • Kotz S, Balakrishnan N, Johnson NL. Continuous multivariate distributions. Vol. 1.. 2nd ed. New York: Wiley-Interscience; 2000. (Wiley Series in Probability and Statistics: Applied Probability and Statistics). DOI:10.1002/0471722065 models and applications
  • Schweizer B, Wolff EF. On nonparametric measures of dependence for random variables. Ann Statist. 1981;9:879–885. doi: 10.1214/aos/1176345528
  • Genest C, Rivest L-P. On the multivariate probability integral transformation. Statist Probab Lett. 2001;53:391–399. DOI: 10.1016/S0167-7152(01)00047-5
  • Wolfram S. The mathematica® book. 4th ed. Champaign, IL: Wolfram Media, Inc.; 1999. Cambridge University Press, Cambridge.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Vienna, Austria; 2008. Available from: http://www.R-project.org ISBN 3-900051-07-0.
  • Johnson ME. Multivariate statistical simulation: a guide to selecting and generating continuous multivariate distributions. New York: John Wiley & Sons; 2013.
  • Armstrong D. Free radicals in diagnostic medicine: a systems approach to laboratory technology, clinical correlations, and antioxidant therapy. Vol. 366. New York: Springer Science & Business Media; 2012.
  • Schisterman EF, Faraggi D, Browne R, et al. Tbars and cardiovascular disease in a population-based sample. J Cardiovasc Risk. 2001;8:219–225. doi: 10.1097/00043798-200108000-00006
  • Feuerverger A. A consistent test for bivariate dependence. Int Stat Rev. 1993;61:419–433. doi: 10.2307/1403753
  • Chen X, Vexler A, Markatou M. Empirical likelihood ratio confidence interval estimation of best linear combinations of biomarkers. Comput Statist Data Anal. 2015;82:186–198. DOI: 10.1016/j.csda.2014.09.010

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