Bayesian estimation of the tail index of a heavy tailed distribution under random censoring

Abdelkader Ameraoui, Kamal Boukhetala and Jean-François Dupuy

Computational Statistics & Data Analysis, 2016, vol. 104, issue C, 148-168

Abstract: Bayesian estimation of the tail index of a heavy-tailed distribution is addressed when data are randomly right-censored. Maximum a posteriori and mean posterior estimators are constructed for various prior distributions of the tail index. Convergence of the posterior distribution of the tail index to a Gaussian distribution is established. Finite-sample properties of the proposed estimators are investigated via simulations. Tail index estimation requires selecting an appropriate threshold for constructing relative excesses. A Monte Carlo procedure is proposed for tackling this issue. Finally, the proposed estimators are illustrated on a medical dataset.

Keywords: Extreme value modeling; Random censoring; Maximum a posteriori estimator; Mean posterior estimator; Asymptotic normality of posterior; Simulations (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:104:y:2016:i:c:p:148-168

DOI: 10.1016/j.csda.2016.06.009

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