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The harmonic moment tail index estimator: asymptotic distribution and robustness

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Abstract

Asymptotic properties of the harmonic moment tail index Estimator are derived for distributions with regularly varying tails. The estimator shows good robustness properties and stands out for its simplicity. A tuning parameter allows for regulating the trade-off between robustness and efficiency. Small sample properties are illustrated by a simulation study.

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References

  • Balkema, A., de Haan, L. (1974). Residual life time at great age. The Annals of Probability, 2, 792–804.

    Google Scholar 

  • Beirlant, J., Goegenbeur, Y., Teugels, J., Segers, J. (2004). Statistics of extremes: theory and applications. Chichester: Wiley.

  • Beirlant, J., Figueiredo, F., Gomes, M., Vandewalle, B. (2008). Improved reduced-bias tail index and quantile estimators. Journal of Statistical Planning and Inference, 138, 1851–1870.

    Google Scholar 

  • Beran, J., Schell, D. (2012). On robust tail index estimation. Computational Statistics and Data Analysis, 56(11), 3430–3443.

    Google Scholar 

  • Billingsley, P. (1968). Convergence of probability measures. New York: Wiley.

  • Brazauskas, V., Serfling, R. (2000). Robust and efficient estimation of the tail index of a single-parameter Pareto distribution. North American Actuarial Journal, 4, 12–27.

    Google Scholar 

  • Cheng, S., Peng, L. (2001). Confidence intervals for the tail index. Bernoulli, 7(5), 751–760.

    Google Scholar 

  • Csörgő, S., Mason, D. M. (1985). Central limit theorems for sums of extreme values. Mathematical Proceedings of the Cambridge Philosophical Society, 98(3), 547–558.

    Google Scholar 

  • Csörgő, S., Viharos, L. (1997). Asymptotic normality of least-squares estimators of tail indices. Bernoulli, (3)3, 351–370.

  • Csörgő, S., Deheuvels, P., Mason, D.M. (1985). Kernel estimates of the tail index of a distribution. The Annals of Statistics, 13, 1050–1077.

    Google Scholar 

  • Davis, R., Resnick, S. (1984). Tail estimates motivated by extreme value theory. The Annals of Statistics, 12, 1467–1487.

    Google Scholar 

  • de Haan, L., Ferreira, A. (2006). Extreme value theory: an introduction. New York: Springer.

  • de Haan, L., Peng, L. (1998). Comparison of tail index estimators. Statistica Neerlandica, 52, 60–70.

    Google Scholar 

  • de Haan, L., Resnick, S. (1998). On asymptotic normality of the hill estimator. Communications in Statistics. Stochastic Models, 14, 849–866.

    Google Scholar 

  • Embrechts, P., Klüppelberg, C., Mikosch, T. (1997). Modelling extremal events. New York: Springer.

  • Fabián, Z., Stehlík, M. (2009). On robust and distribution sensitive Hill like method. IFAS research report 43. Linz: Department for Applied Statistics, Johannes Kepler University Linz.

  • Ferreira, A., de Vries, C. (2004). Optimal confidence intervals of the tail index and high quantiles. Discussion paper TI 2004-090/2. The Netherlands: Tinbergen Institute.

  • Finkelstein, M., Tucker, H.G., Veeh, J.A. (2006). Pareto tail index estimation revisited. North American Actuarial Journal, 10(1), 1–10.

    Google Scholar 

  • Haeusler, E., Teugels, J.L. (1985). On asymptotic normality of Hill’s estimator for the exponent of regular variation. The Annals of Statistics, 13(2), 743–756.

    Google Scholar 

  • Hall, P. (1982). On some simple estimates of an exponent of regular variation. Journal of the Royal Statistical Society: Series B, 44(1), 37–42.

    Google Scholar 

  • Hampel, F. (1968). Contributions to the theory of robust estimation. Ph.D. thesis, Berkeley: Department of Statistics, University of California.

  • Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A. (1986). Robust statistics: the approach based on influence functions. New York: Wiley.

  • Henry III, J.B. (2009). A harmonic moment tail index estimator. Journal of Statistical Theory and Applications, 8(2), 141–162.

  • Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3(5), 1163–1174.

    Google Scholar 

  • Juárez, S.F., Schucany, W.R. (2004). Robust and efficient estimation for the generalized Pareto distribution. Extremes, 7, 237–251.

    Google Scholar 

  • Knight, K. (2012). A simple modification of the Hill estimator with applications to robustness and bias reduction (preprint).

  • Lu, J.-C., Peng, L. (2002). Likelihood based confidence intervals for the tail index. Extremes, 5, 337–352.

    Google Scholar 

  • Mason, D. (1982). Laws of large numbers of sums of extreme values. The Annals of Probability, 10, 168–177.

    Google Scholar 

  • Peng, L., Welsh, A. (2002). Robust estimation of the generalized Pareto distribution. Extremes, 4, 53–65.

    Google Scholar 

  • Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 119–131.

  • Qi, Y. (2008). Bootstrap and empirical likelihood methods in extremes. Extremes, 11, 81–97.

    Google Scholar 

  • Reiss, R., Thomas, M. (2005). Statistical analysis of extreme values (for insurance, finance, hydrology and other fields) (3rd rev ed.). Basel: Birkhäuser.

  • Resnick, S. (2007). Heavy-tail phenomena: probabilistic and statistical modeling. New York: Springer.

  • Stehlík, M., Potocký, R., Waldl, H., Fabián, Z. (2010). On the favourable estimation of fitting heavy tailed data. Computational Statistics, 25, 485–503.

    Google Scholar 

  • Stehlík, M., Fabián, Z., Střelec, L. (2012). Small sample robust testing for normality against Pareto tails. Communications in Statistics: Simulation and Computation, 41(7), 1167–1194.

    Google Scholar 

  • Vandewalle, B. (2004). Some robust and semi-parametric methods in extreme value theory. Doctoral thesis, Leuven: Department of Mathematics, Katholieke Universiteit Leuven.

  • Vandewalle, B., Beirlant, J., Hubert, M. (2004). A robust estimator of the tail index based on an exponential regression model. In M. Hubert, G. Pison, A. Struyf, S. van Aelst (Eds.), Theory and applications of recent robust methods (pp. 367–376). Basel: Birkhauser.

  • Vandewalle, B., Beirlant, J., Christmann, A., Hubert, M. (2007). A robust estimator for tail index of Pareto-type distributions. Computational Statistics and Data Analysis, 51, 6252–6268.

    Google Scholar 

  • Weissman, I. (1978). Estimation of parameters and larger quantiles based on the k largest observations. Journal of the American Statistical Association, 73(364), 812–815.

    Google Scholar 

  • Worms, J., Worms, R. (2011). Empirical likelihood based confidence regions for first order parameters of heavy-tailed distributions. Journal of Statistical Planning and Inference, 141(8), 2769–2786.

    Google Scholar 

Download references

Acknowledgments

We would like to thank a referee and the associate editor for their constructive remarks that led to an improved presentation of the results. This research has been supported by the DFG (grant BE 2123/10-1).

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Correspondence to Jan Beran.

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Beran, J., Schell, D. & Stehlík, M. The harmonic moment tail index estimator: asymptotic distribution and robustness. Ann Inst Stat Math 66, 193–220 (2014). https://doi.org/10.1007/s10463-013-0412-2

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  • DOI: https://doi.org/10.1007/s10463-013-0412-2

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