Robust Extreme Quantile Estimation for Pareto-Type tails through an Exponential Regression Model

Richard Minkah, Tertius de Wet and Abhik Ghosh

No hf7vk, AfricArxiv from Center for Open Science

Abstract: The estimation of extreme quantiles is one of the main objectives of statistics of extremes ( which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error. Practical application of the proposed estimator is illustrated with data from pedochemical and insurance industries.

Date: 2022-03-25
New Economics Papers: this item is included in nep-ecm, nep-ias, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:osf:africa:hf7vk

DOI: 10.31219/osf.io/hf7vk

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