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Advanced Statistical Analysis

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Statistical Analysis of Extreme Values
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Abstract

Section 6.1 provides a discussion about non-random and random censoring. Especially, the sample df is replaced by the Kaplan-Meier estimate in the case of randomly censored data. In Section 6.2 we continue the discussion of Section 2.7 about the clustering of exceedances by introducing time series models and the extremal index. The insight gained from time series such as moving averages (MA), autoregressive (AR) and ARMA series will also be helpful for the understanding of time series like ARCH and GARCH which provide special models for financial time series, see Sections 16.7 and 16.8.

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References

  1. For relevant results see Balakrishnan, N. and Cohen, A.C. (1991). Order Statistics and Inference. Academic Press, Boston.

    MATH  Google Scholar 

  2. See, e.g., Shorack, G.R. and Wellner, J.A. (1986). Empirical Processes with Applications to Statistics. Wiley, New York.

    Google Scholar 

  3. Leadbetter, M.R. and Nandagopalan, S. (1989). On exceedance point processes for stationary sequences under mild oscillation restrictions. In Mikosch, and Resnick, Birkhäuser, Basel [14]}, pp. 69–80.

    Google Scholar 

  4. Chernick, M.R., Hsing, T. and McCormick, W.P. (1991). Calculating the extremal index for a class of stationary sequences. Adv. Appl. Prob. 23, 835–850.

    Article  MATH  Google Scholar 

  5. Davis, R.A. and Resnick, S.I. (1985). Limit theory for moving averages of random variables with regular varying tail probabilities. Ann. Prob. 13, 179–195.

    MATH  Google Scholar 

  6. Hsing, T., Hüsler, J. and Reiss, R.-D. (1996). The extremes of a triangular array of normal random variables. Ann. Appl. Prob. 6, 671–686.

    Article  MATH  Google Scholar 

  7. The basic algorithms are described in Nolan, J.P. (1997). Numerical computation of stable densities and distribution functions. Commun. Statist.-Stochastic Models 13, 759–774. Note that the STABLE package is no longer included in Xtremes.

    Article  MATH  Google Scholar 

  8. Nolan, J.P. (1998). Parameterizations and modes of stable distributions. Statist. Probab. Letters 38, 187–195.

    Article  Google Scholar 

  9. Zolotarev, V.M. (1986). One-Dimensional Stable Distributions. Translations of Mathematical Monographs, Vol. 65, American Mathematical Society.

    Google Scholar 

  10. Fofack, H. and Nolan, J.P. (1999). Tail behavior, modes and other characteristics of stable distributions. Extremes 2, 39–58.

    Article  MATH  Google Scholar 

  11. McCulloch, J.H. (1986). Simple consistent estimators of stable distribution parameters. Commun. Statist. Simulations 15, 1109–1136.

    Article  MATH  Google Scholar 

  12. Kogon, S.M. and Williams, D.B. (1998). Characteristic function based estimation of stable distribution parameters. In: A Practical Guide to Heavy Tails, R.J. Adler et all (eds.), Birkhäuser, Basel, 311–335.

    Google Scholar 

  13. Nolan, J.P. (2001). Maximum likelihood estimation of stable parameters. In: Lévy Processes, ed. by Barndorff-Nielson, Mikosch, and Resnick, Birkhäuser, Basel, 379–400.

    Google Scholar 

  14. Communicated by S. Caserta and C.G. de Vries and stored in blackmarket.dat.

    Google Scholar 

  15. Akgiray, V., Booth, G.G. and Seifert, B. (1988). Distribution properties of Latin American black market exchange rates. J. Int. Money and Finance 7, 37–48.

    Article  Google Scholar 

  16. Koedijk, K.G., Stork, P.A. and Vries, de C.G. (1992). Differences between foreign exchange rate regimes: the view from the tail. J. Int. Money and Finance 11, 462–473.

    Article  Google Scholar 

  17. This condition can be attributed to L. Weiss (1971), see the article cited on page 134.

    Google Scholar 

  18. See, e.g., Goldie, C.M. and Smith, R.L. (1987). Slow variation with remainder term: A survey of the theory and its applications. Quart. J. Math. Oxford Ser. 38, 45–71.

    Article  MATH  Google Scholar 

  19. Hall, P. (1982). On estimating the endpoint of a distribution. Ann. Statist. 10, 556–568.

    MATH  Google Scholar 

  20. Hall, P. and Welsh, A.H. (1985). Adaptive estimates of parameters of regular variation. Ann. Statist. 13, 331–341.

    MATH  Google Scholar 

  21. Kaufmann, E. and Reiss, R.-D. (2002). An upper bound on the binomial process approximation to the exceedance process. Extremes 5, 253–269.

    Article  Google Scholar 

  22. Kaufmann, E. (2000). Penultimate approximations in extreme value theory. Extremes 3, 39–55.

    Article  Google Scholar 

  23. Stored in the file um-lspge.dat (communicated by G.R. Heer, Federal Statistical Office).

    Google Scholar 

  24. Falk, M. and Marohn, F. (1993). Von Mises conditions revisited. Ann. Probab. 21, 1310–1328, and Kaufmann, E. (1995). Von Mises conditions, δ-neighborhoods and rates of convergence for maxima. Statist. Probab. Letters 25, 63–70.

    MATH  Google Scholar 

  25. Radtke, M. (1988). Konvergenzraten und Entwicklungen unter von Mises Bedingungen der Extremwerttheorie. Ph.D. Thesis, University of Siegen, (also see, e.g., [42], page 199).

    Google Scholar 

  26. de Haan, L. and Resnick, S.I. (1996). Second order regular variation and rates of convergence in extreme value theory. Ann. Probab. 24, 97–124.

    Article  MATH  Google Scholar 

  27. Gomes, M.I. (1984). Penultimate limiting forms in extreme value theory. Ann. Inst. Statist. Math. 36, Part A, 71–85, and Gomes, M.I. (1994). Penultimate behaviour of the extremes. In [15], Vol. 1, 403–418.

    Article  MATH  Google Scholar 

  28. Gomes, M.I. and Haan, L. de (1999). Approximation by penultimate extreme value distributions. Extremes 2, 71–85.

    Article  MATH  Google Scholar 

  29. Reiss, R.-D. (1989). Extreme value models and adaptive estimation of the tail index. In Mikosch, and Resnick, Birkhäuser, Basel [14]}, 156–165.

    Google Scholar 

  30. Gomes, M.I. (1994). Metodologias Jackknife e Bootstrap em Estatística de Extremos. In Mendes-Lopes et al. (eds.), Actas II Congresso S.P.E., 31–46.

    Google Scholar 

  31. Drees, H. (1996). Refined Pickands estimators with bias correction. Comm. Statist. Theory and Meth. 25, 837–851.

    Article  MATH  Google Scholar 

  32. Peng, L. (1998). Asymptotically unbiased estimator for the extreme-value index. Statist. Probab. Letters 38, 107–115.

    Article  Google Scholar 

  33. Martins, M.J., Gomes, M.I. and Neves, M. (1999). Some results on the behavior of Hill estimator. J. Statist. Comput. and Simulation 63, 283–297.

    Article  MATH  Google Scholar 

  34. Beirlant, J., Dierckx, G., Gogebeur, Y. and Matthys, G. (1999). Tail index estimation and an exponential regression model. Extremes 2, 177–200.

    Article  MATH  Google Scholar 

  35. Feuerverger, A. and Hall, P. (1999). Estimating a tail exponent by modelling departure from a Pareto distribution. Ann. Statist. 27, 760–781.

    Article  MATH  Google Scholar 

  36. Gomes, M.I., Martins, M.J. and Neves, M. (2000). Alternatives to a semi-parametric estimator of parameters of rare events-the Jackknife methodology. Extremes 3, 207–229, and Gomes, M.I., Martins, M.J. and Neves, M. (2002). Generalized Jackknife semi-parametric estimators of the tail index. Portugaliae Mathematica 59, 393–408.

    Article  MATH  Google Scholar 

  37. Beirlant, J., Vynckier, P. and Teugels, J.L. (1996). Excess function and estimation of the extreme-value index. Bernoulli 2, 293–318.

    Article  MATH  Google Scholar 

  38. Fraga Alves, M.I., Gomes, M.I. and de Haan, L. (2003). A new class of semi-parametric estimators of the second order parameter. Portugaliae Mathematica 60, 193–213.

    MATH  Google Scholar 

  39. Caeiro, F., Gomes, M.I. and Pestana, D. (2005). Direct reduction of bias of the classical Hill estimator. Revstat 3, 111–136.

    Google Scholar 

  40. Gomes, M.I. and Pestana, D. (2004). A simple second order reduced-bias’ tail index estimator. J. Statist. Comp. and Simulation, in press.

    Google Scholar 

  41. Beirlant, J., Dierckx, G., Guillou, A. and Starica, C. (2002). On exponential representations of log-spacings of extreme order statistics. Extremes 5, 157–180.

    Article  MATH  Google Scholar 

  42. Kaufmann, E. and Reiss, R.-D. (1998). Approximation of the Hill estimator process. Statist. Probab. Letters 39, 347–354.

    Article  MATH  Google Scholar 

  43. Drees, H., de Haan, L. and Resnick, S.I. (2000). How to make a Hill plot. Ann. Statist. 28, 254–274.

    Article  MATH  Google Scholar 

  44. Gomes, M.I. and Martins, M.J. (2002). “Asymptotically unbiased” estimators of the tail index based on external estimation of the second order parameter. Extremes 5, 5–31.

    Article  MATH  Google Scholar 

  45. Gomes, M.I., Martins, M.J. and Neves, M. (2005). Revisiting the second order reduced bias “maximum likelihood” extreme value index estimators. Notas e Comunicações CEAUL 10/2005. Submitted.

    Google Scholar 

  46. Gomes, M.I., de Haan, L. and Rodrigues, L. (2004). Tail index estimation through accommodation of bias in the weighted log-excesses. Notas e Comunicações CEAUL 14/2004. Submitted.

    Google Scholar 

  47. Gomes, M.I., Caeiro, F. and Figueiredo, F. (2004). Bias reduction of a tail index estimator through an external estimation of the second order parameter. Statistics 38, 497–510.

    MATH  Google Scholar 

  48. Gomes, M.I. and Martins M.J. (2001). Alternatives to Hill’s estimator-asymptotic versus finite sample behaviour. J. Statist. Planning and Inference 93, 161–180.

    Article  MATH  Google Scholar 

  49. Fraga Alves, M.I. (2001). A location invariant Hill-type estimator. Extremes 4, 199–217.

    Article  MATH  Google Scholar 

  50. Gomes, M.I., Figueiredo, F. and Mendonça, S. (2005). Asymptotically best linear unbiased tail estimators under a second order regular variation condition. J. Statist. Plann. Inf. 134, 409–433

    Article  MATH  Google Scholar 

  51. Gomes, M.I., Miranda, C. and Viseu, C. (2006). Reduced bias tail index estimation and the Jackknife methodology. Statistica Neerlandica 60, 1–28.

    Article  Google Scholar 

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(2007). Advanced Statistical Analysis. In: Statistical Analysis of Extreme Values. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7399-3_6

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