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BIAS REDUCED ESTIMATION OF THE EXTREME QUANTILE FOR HEAVY TAILED DISTRIBUTIONS OF RANDOM RIGHT TRUNCATED DATA
Keywords:
estimation, extreme quantile, heavy-tails, random truncationDOI:
https://doi.org/10.17654/0972361724025Abstract
In this paper, we propose a bias reduced tail index estimator of randomly right truncated data that belongs to the Fréchet domain of attraction. Our approach is based on the combination of two kernel-type estimators of the extreme value index to cancel the bias. The resulting estimator is used to estimate an extreme quantile. We show by simulation that the proposed estimator behaves well in terms of bias and mean square error that alternative estimators recently introduced in the literature. Our methodology is applied to a real dataset of lifetime of automobile brake pads by numerical simulation.
Received: January 8, 2024
Accepted: March 2, 2024
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