Abstract
Recent developments on the Hill and related estimators of the extreme value index are provided. We also discuss their properties, like mean square error, efficiency and robustness. We further discuss the introduction of underlying score functions related to modified Hill estimators.
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References
Araújo Santos, P., Fraga Alves, M.I., Gomes, M.I.: Peaks over random threshold methodology for tail index and quantile estimation. Revstat 4, 227–247 (2006)
Beirlant, J., Vynckier, P., Teugels, J.: Excess functions and estimation of the extreme-value index. Bernoulli 2, 293–318 (1996)
Beirlant, J., Dierckx, G., Guillou, A.: Estimation of the extreme-value index and generalized quantile plots. Bernoulli 11(6), 949–970 (2005)
Beirlant, J., Caeiro, C., Gomes, M.I.: An overview and open research topics in the field of statistics of univariate extremes. Revstat 10(1), 1–31 (2012)
Beran, J., Schell, D., Stehlík, M.: The harmonic moment tail index estimator: asymptotic distribution and robustness. Ann. Inst. Stat. Math. 66, 193–220 (2014)
Bingham, N., Goldie, C.M., Teugels, J.L.: Regular Variation. Cambridge Univ. Press, Cambridge (1987)
Brazauskas, V., Serfling, R.: Robust and efficient estimation of the tail index of a single-parameter Pareto distribution. North Am. Actuar. J. 4, 12–27 (2000)
Brazauskas, V., Serfling, R.: Robust estimation of tail parameters for two-parameter Pareto and exponential models via generalized quantile statistics. Extremes 3(3), 231–249 (2000).
Brilhante, M.F., Gomes, M.I., Pestana, D.: A simple generalization of the Hill estimator. Comput. Stat. Data Anal. 57, 518–535 (2013)
Caeiro, F., Gomes, M.I., Pestana, D.: Direct reduction of bias of the classical Hill estimator. Revstat 3, 113–136 (2005)
Caeiro, F., Gomes, M.I., Henriques-Rodrigues, L.: Reduced-bias tail index estimators under a third order framework. Commun. Stat. Theory Methods 38, 1019–1040 (2009)
Dobrovidov, A.V., Koshkin, G.M. and Vasiliev, V.A.: Non-Parametric State Space Models. Kendrick Press, Heber City (2012)
Edgeworth, F.Y.: On the probable errors of frequency-constants (contd.). J.R. Stat. Soc. 71, 499–512 (1908)
Fabián Z.: Induced cores and their use in robust parametric estimation. Commun. Stat. Theory Methods 30, 537–556 (2001)
Fabián, Z., Stehlík, M.: A note on favorable estimation when data is contaminated. Commun. Dependability Qual. Manag. 114, 36–43 (2008)
Fisher, R.A.: Theory of statistical estimation. Procs. Camb. Philos. Soc. 22(700725), doi: 10.1017/S0305004100009580 (1925)
Fraga Alves, M.I.: A location invariant Hill-type estimator. Extremes 4(3), 199–217 (2001)
Fraga Alves, M.I., Gomes, M.I., de Haan, L. Neves, C.: Mixed moment estimators and location invariant alternatives. Extremes 12, 149–185 (2009)
Gomes, M.I., Pestana, D.: A sturdy reduced bias extreme quantile (VaR) estimator. J. Am. Stat. Assoc. 102(477), 280–292 (2007)
Gomes, M.I., Martins, M.J., Neves, M.M.: Improving second order reduced bias extreme value index estimation. Revstat 5(2), 177–207 (2007)
Gomes, M.I., Canto e Castro, L., Fraga Alves, M.I., Pestana, D.: Statistics of extremes for iid data and breakthroughs in the estimation of the extreme value index: Laurens de Haan leading contributions. Extremes 11(1), 3–34 (2008)
Gomes, M.I., Fraga Alves, M.I. Araújo Santos, P.: PORT Hill and moment estimators for heavy-tailed models. Commun. Stat. Simul. Comput. 37, 128–1306 (2008)
Gomes, M.I., de Haan, L., Henriques-Rodrigues, L.: Tail Index estimation for heavy-tailed models: accommodation of bias in weighted log-excesses. J. R. Stat. Soc. B 70(1), 31–52 (2008)
Gomes, M.I., Henriques-Rodrigues, L. and Miranda, C.: Reduced-bias location-invariant extreme value index estimation: a simulation study. Commun. Stat. Simul. Comput. 40(3), 424–447 (2011)
Gomes, M.I., Martins, M.J., Neves, M.M.: Generalised jackknife-based estimators for univariate extreme-value modelling. Commun. Stat. Theory Methods 42(7), 1227–1245 (2013)
Gomes, M.I., Henriques-Rodrigues, L., Fraga Alves, M.I. and Manjunath, B.G.: Adaptive PORT-MVRB estimation: an empirical comparison of two heuristic algorithms. J. Stat. Comput. Simul. (2013). doi:10.1080/00949655.2011.652113
de Haan, L., Ferreira, A.: Extreme Value Theory: An Introduction. Springer, New York (2006)
Hill, B.: A simple general approach to inference about the tail of a distribution. Ann. Stat. 3(5), 1163–1174 (1975)
Johnson, N.L.: Systems of frequency curves generated by methods of translations, Biometrika 36, 149–176 (1949)
Pearson, K., Filon, L.N.G.: Mathematical contributions to the theory of evolution. IV. On the probable errors of frequency constants and on the influence of random selection on variation and correlation. Philos. Trans. R. Soc. Lond. A 191, 229–311 (1898). Reprinted in Karl Pearsons Early Statistical Papers (1956) 179–261; Cambridge Univ. Press. Abstr. Proc. R. Soc. Lond. 62, 173–176 (1897)
Stehlík, M., Fabián, Z., Střelec, L.: Small sample robust testing for Normality against Pareto tails. Commun. Stat. Simul. Comput. 41, 1167–1194 (2012)
Stehlík, M., Potocký, R., Waldl, H., Fabián, Z.: On the favourable estimation of fitting heavy tailed data. Comput. Stat. 25, 485–503 (2010)
Vandewalle, B., Beirlant, J., Christmann, A., Hubert, M.: A robust estimator for the tail index of Pareto-type distributions, Comput. Stat. Data Anal. 51, 6252–6268 (2007)
Acknowledgements
M. Ivette Gomes has been partially supported by National Funds through FCT—Fundação para a Ciência e a Tecnologia, projects PEst-OE/MAT/UI0006/2011 (CEAUL) and EXTREMA (PTDC/MAT/101736/2008).
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Gomes, M.I., Stehlík, M. (2014). The Latest Advances on the Hill Estimator and Its Modifications. In: Akritas, M., Lahiri, S., Politis, D. (eds) Topics in Nonparametric Statistics. Springer Proceedings in Mathematics & Statistics, vol 74. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0569-0_29
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DOI: https://doi.org/10.1007/978-1-4939-0569-0_29
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