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Dependence Measures for Extreme Value Analyses

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Abstract

Quantifying dependence is a central theme in probabilistic and statistical methods for multivariate extreme values. Two situations are possible: one where, in a limiting sense, the extremes are dependent; the other where, in the same sense, the extremes are independent. This paper comprises an overview of the principal issues through a unified approach which encompasses both these situations. Novel diagnostic measures for dependence are also developed which provide complementary information about different aspects of extremal dependence. The paper is written in an elementary style, with the methodology illustrated by application to theoretical examples and typical data-sets. These data-sets and the S-plus functions used for the analyses are available online.

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References

  • Barão, M.I. and Tawn, J.A., “Extremal analysis of short series with outliers: sea-levels and athletics records,” Appl. Statist. 48, 469–487, (1999).

    Google Scholar 

  • Bortot, P. and Tawn, J.A., “Models for the extremes of Markov chains,” Biometrika 85, 851–867, (1998).

    Google Scholar 

  • Bortot, P., Coles, S.G., and Tawn, J.A., “The multivariate Gaussian tail model: an application to oceanographic data,” Appl. Statist., 49, 31–49 (2000).

    Google Scholar 

  • Bruun, J.T. and Tawn, J.A., “Comparison of approaches for estimating the probability of coastal flooding,” Appl. Statist. 47, 405–423, (1998).

    Google Scholar 

  • Capérá, P., Fougéres, A.-L., and Genest, C., “Estimation of bivariate extreme value copulas,” Biometrika 84, 567–577, (1997).

    Google Scholar 

  • Coles, S.G., “A temporal study of extreme rainfall,” in Statistics for the Environment 2:Water Related Issues, (V. Barnett and K.F. Turkman, eds.), Wiley, Chichester, 61–78, (1994).

    Google Scholar 

  • Coles, S.G. and Tawn, J.A., “Modelling extreme multivariate events,” J. R. Statist. Soc. B 53, 377–392, (1991).

    Google Scholar 

  • Coles, S.G. and Tawn, J.A., “Statistical methods for multivariate extremes: an application to structural design (with discussion),” Appl. Statist. 43, 1–48, (1994).

    Google Scholar 

  • Coles, S.G. and Tawn, J.A., “A Bayesian analysis of extreme rainfall data,” Appl. Statist. 45, 463–478, (1996).

    Google Scholar 

  • Currie, J.E., “Directory of coefficients of tail dependence,” Department of Mathematics and Statistics Technical Report, ST-99-06, Lancaster University, 1999.

  • Davison, A.C. and Smith, R.L., “Models for exceedances over high thresholds (with discussion),” J. R. Statist. Soc. B 52, 393–442, (1990).

    Google Scholar 

  • Dekkers, A.L.M., Einmahl, J.H.J., and de Haan, L., “A moment estimator for the index of an extreme value distribution,” Ann. Statist. 17, 1833–1855, (1989).

    Google Scholar 

  • Einmahl, J.H.J., de Haan, L. and Sinha, A.K., “Estimating the spectral measure of an extreme value distribution,” Stoch. Proc. Appl. 70, 143–171, (1997).

    Google Scholar 

  • Geffroy, J., “Contributions ála théorie des valeurs extrème,” Publ. Inst. Statist. Univ. Paris 7/8, 37–185, (1958/59).

    Google Scholar 

  • de Haan, L., “Extremes in higher dimensions: the model and some statistics.” In Proc. 45th Sess. Int. Statist. Inst., paper 26.3. International Statistical Institute, The Hague, 1985.

    Google Scholar 

  • de Haan, L. and de Ronde, J., “Sea and wind: multivariate extremes at work,” Extremes 1, 7–45, (1998).

    Google Scholar 

  • Joe, H., “Families of min-stable multivariate exponential and multivariate extreme value distributions,” Statist. Probab. Lett. 9, 75–81, (1990).

    Google Scholar 

  • Joe, H., “Multivariate extreme-value distributions with applications to environmental data,” Canadian. J. Statist 22, 47–64, (1994).

    Google Scholar 

  • Joe, H., Multivariate Models and Dependence Concepts, Chapman & Hall, London, 1997.

    Google Scholar 

  • Joe, H., Smith, R.L., and Weissman, I., “Bivariate threshold methods for extremes,” J. R. Statist. Soc. B 54, 171–183, (1992).

    Google Scholar 

  • Leadbetter, M.R., Lindgren, G., and Rootzén, H., Extremes and Related Properties of Random Sequences and Series, Springer Verlag, New York, 1983.

    Google Scholar 

  • Ledford, A.W., “Extreme values of the bivariate normal distribution,” Submitted, 1999.

  • Ledford, A.W. and Tawn, J.A., “Statistics for near independence in multivariate extreme values,” Biometrika 83, 169–187, (1996).

    Google Scholar 

  • Ledford, A.W. and Tawn, J.A., “Modelling dependence within joint tail regions,” J. R. Statist. Soc. B 59, 475–499, (1997).

    Google Scholar 

  • Ledford, A.W. and Tawn, J.A., “Concomitant tail behavior for extremes,” Adv. Appl. Probab. 30, 197–215, (1998).

    Google Scholar 

  • Ledford, A.W. and Tawn, J.A., “Diagnostics for dependence within time-series extremes,” Submitted to J. R. Statist. Soc. B (2000).

  • Mardia, K.V., “Asymptotic independence of bivariate extremes,” Calcutta Statist. Assoc. Bull. 13, 172–178, (1964).

    Google Scholar 

  • Nandagopalan, S., “On the multivariate extremal index,” Jnl of Research, National Inst. of Standards and Technology 99, 543–550, (1994).

    Google Scholar 

  • Nelsen, R.B., An Introduction to Copulas, Springer-Verlag, New York, 1998.

    Google Scholar 

  • Peng, L., “Estimation of the coefficient of tail dependence in bivariate extremes,” Statist. Probab. Lett. 43, 399–409, (1999).

    Google Scholar 

  • Pickands, J., “Multivariate extreme value distributions,” Bull. Int. Statist. Inst. 49, 859–878, (1981).

    Google Scholar 

  • Resnick, S.I., Extreme Values, Point Processes and Regular Variation, Springer-Verlag, New York, 1987.

    Google Scholar 

  • Sibuya, M., “Bivariate extreme statistics,” Ann. Inst. Statist. Math. 11, 195–210, (1960).

    Google Scholar 

  • Shi, D., Smith, R.L., and Coles, S.G., “Joint versus marginal estimation for bivariate extremes,” Department of Statistics Technical Report, 2074, University of North Carolina at Chapel Hill, 1992.

    Google Scholar 

  • Smith, R.L., “Extreme value theory,” In Handbook of Applicable Mathematics, (W. Ledermann, ed), 7, chapter 14, 437–472, Chichester, Wiley, 1990.

    Google Scholar 

  • Smith, R.L., “Multivariate threshold methods.” In Extreme Value Theory & Applications, (J. Galambos, J. Lechner and E. Simiu, eds) Kluwer, Dordrecht, 225–248, (1994).

    Google Scholar 

  • Smith, R.L., Tawn, J.A., and Coles, S.G., “Markov chain models for thresholds exceedances,” Biometrika 84, 249–268, (1997).

    Google Scholar 

  • Tawn, J.A., “Bivariate extreme value theory: models and estimation,” Biometrika 75, 397–415, (1988).

    Google Scholar 

  • Tiago de Oliveira, J., “Structure theory of bivariate extremes, extensions,” Est. Mat., Estat. e. Econ. 7, 165–195, (1962/63).

    Google Scholar 

  • Weintraub, K.S., “Sample and ergodic properties of some min-stable processes,” Annals Probab. 19, 706–723, (1991).

    Google Scholar 

  • Yun, S., “The extremal index of a higher-order stationary Markov chain,” Ann. Appl. Probab. 8, 408–437, (1998).

    Google Scholar 

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Coles, S., Heffernan, J. & Tawn, J. Dependence Measures for Extreme Value Analyses. Extremes 2, 339–365 (1999). https://doi.org/10.1023/A:1009963131610

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