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The generalization of several classical estimators for a positive extreme value index
Marijus Vaičiulis ORCID icon link to view author Marijus Vaičiulis details  

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https://doi.org/10.15559/26-VMSTA296
Pub. online: 19 March 2026      Type: Research Article      Open accessOpen Access

Received
6 September 2025
Revised
9 February 2026
Accepted
26 February 2026
Published
19 March 2026

Abstract

In this paper, we introduce a family of semi-parametric estimators for the positive extreme value index γ, parameterized in two tuning parameters. The asymptotic normality of the introduced estimators is proved. It is shown that the partial case of newly introduced estimators (a subfamily with one tuning parameter) has quite good asymptotic properties and dominates several previously introduced estimators. Small Monte-Carlo simulations are included. Also, the performance of this parameterized subfamily of estimators is illustrated for pair exchange ratio data sets.

References

[1] 
Brilhante, M.F., Gomes, M.I., Pestana, D.: A simple generalization of the Hill estimator. Comput. Stat. Data Anal. 57, 518–535 (2013) MR2981106. https://doi.org/10.1016/j.csda.2012.07.019
[2] 
Caeiro, F., Gomes, M.I.: Minimum-variance reduced-bias tail index and high quantile estimation. REVSTAT 6, 1–20 (2008) MR2386296
[3] 
Caeiro, F., Gomes, M.I.: Bias reduction in the estimation of parameters of rare events. Theory Stoch. Process. 8, 67–76 (2002) MR2026256
[4] 
Caeiro, F., Gomes, M.I.: A class of asymptotically unbiased semi-parametric estimators of the tail index. Test 11, 345–364 (2002) MR1947602. https://doi.org/10.1007/BF02595711
[5] 
De Haan, L., Ferreira, A.: Extreme Value Theory: an Introduction. Springer, New York (2006) MR2234156. https://doi.org/10.1007/0-387-34471-3
[6] 
De Haan, L., Peng, L.: Comparison of tail index estimators. Stat. Neerl. 52, 60–70 (1998) MR1615558. https://doi.org/10.1111/1467-9574.00068
[7] 
Dekkers, A.L.M., de Haan, L.: Optimal choice of sample fraction in extreme-value estimation. J. Multivar. Anal. 47, 173–195 (1993) MR1247373. https://doi.org/10.1006/jmva.1993.1078
[8] 
Dekkers, A.L.M., Einmahl, J.H.J., de Haan, L.: A moment estimator for the index of an extreme-value distribution. Ann. Stat. 17, 1833–1855 (1989) MR1026315. https://doi.org/10.1214/aos/1176347397
[9] 
Draisma, G., de Haan, L., Peng, L., Pereira, T.T.: A bootstrap-based method to achieve optimality in estimating the extreme-value index. Extremes 2, 367–404 (1999) MR1776855. https://doi.org/10.1023/A:1009900215680
[10] 
Fedotenkov, I.: A review of more than one hundred pareto-tail index estimators. Statistica 80, 245–299 (2020)
[11] 
Fraga Alves, M.I., Gomes, M.I., de Haan, L.: A new class of semi-parametric estimators of the second order parameter. Port. Math. 60, 193–214 (2003) MR1984031
[12] 
Gomes, M.I., Martins, M.J.: Efficient alternatives to the Hill estimator. In: Proceedings of the Workshop V.E.L.A. - Extreme Values and Additive Laws, pp. 40–43 (1999)
[13] 
Gomes, M.I., Martins, M.J.: Alternatives to Hill’s estimator - asymptotic versus finite sample behaviour. J. Stat. Plan. Inference 93, 161–180 (2001) MR1822394. https://doi.org/10.1016/S0378-3758(00)00201-9
[14] 
Gomes, M.I., Martins, M.J.: Asymptotically unbiased estimators of the tail index based on external estimation of the second order parameter. Extremes 5, 5–31 (2002) MR1947785. https://doi.org/10.1023/A:1020925908039
[15] 
Gomes, M.I., Pestana, D., Caeiro, F.: A note on the asymptotic variance at optimal levels of a bias-corrected hill estimator. Stat. Probab. Lett. 79, 295–303 (2009) MR2493012. https://doi.org/10.1016/j.spl.2008.08.016
[16] 
Hall, P.: On some simple estimates of an exponent of regular variation. J. R. Stat. Soc. Ser. B 44, 37–42 (1982) MR0655370
[17] 
Hall, P., Welsh, A.H.: Adaptive estimates of parameters of regular variation. Ann. Stat. 13, 331–341 (1985) MR0773171. https://doi.org/10.1214/aos/1176346596
[18] 
Hill, B.M.: A simple general approach to inference about the tail of a distribution. Ann. Stat. 3, 1163–1174 (1975) MR0378204
[19] 
Oliveira, O.A., Gomes, M.I., Fraga Alves, M.I.: Improvement in the estimation of a heavy tails. REVSTAT 4, 81–109 (2006) MR2259366
[20] 
Paulauskas, V., Vaičiulis, M.: On the improvement of Hill and some others estimators. Lith. Math. J. 53, 336–355 (2013) MR3097309. https://doi.org/10.1007/s10986-013-9212-x
[21] 
Paulauskas, V., Vaičiulis, M.: Comparison of the several parameterized estimators for the positive extreme value index. J. Stat. Comput. Simul. 87, 1342–1362 (2016) MR3621952. https://doi.org/10.1080/00949655.2016.1263303
[22] 
Penalva, H., Caeiro, F., Gomes, M.I., Neves, M.M.: An efficient naive generalisation of the Hill estimator: discrepancy between asymptotic and finite sample behaviour. Notas e Comuniçaoes CEAUL 2 (2016)
[23] 
Vaičiulis, M.: A multivariate limit theorem for generalized Hill statistics. Lith. Math. J. 65, 117–133 (2025) MR4885701. https://doi.org/10.1007/s10986-025-09658-2
[24] 
Van Der Vaart, A.W.: Asymptotic Statistics. Cambridge University Press (1998) MR1652247. https://doi.org/10.1017/CBO9780511802256

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Keywords
Asymptotic normality extreme value index Hall class Hill estimator

MSC2020
62F12 62G32 60F05

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