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A class of semiparametric tail index estimators and its applications. (English. Russian original) Zbl 1436.62125

Autom. Remote Control 80, No. 10, 1803-1816 (2019); translation from Avtom. Telemekh. 2019, No. 10, 62-77 (2019).
Summary: A new class of semiparametric estimators of the tail index is proposed. These estimators are based on a rather general class of semiparametric statistics. Their asymptotic normality is proved. The new estimators are compared with several other recently introduced estimators of the tail index in terms of the asymptotic mean-square error. An algorithm to calculate the new estimators is developed and then applied to several real data sets.

MSC:

62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
62G08 Nonparametric regression and quantile regression
62G30 Order statistics; empirical distribution functions
62P05 Applications of statistics to actuarial sciences and financial mathematics

Software:

SNAP
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References:

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