Estimating the end-point of a probability distribution using minimum-distance methods

DOI10.2307/3318618zbMath0917.62013OpenAlexW2036880950MaRDI QIDQ1283390

Julian Z. Wang, Hall, Peter

Publication date: 5 July 1999

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1173707100

zbMATH Keywords

central limit theoremdomain of attractionextreme value theoryWeibull distributionPareto distributioncoefficient of determinationgoodness of fitGreenwood's statisticleast-squares maximum-likelihood order statisticsporting records

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Point estimation (62F10)

Related Items (12)

Maximum likelihood estimation of extreme value index for irregular cases ⋮ Bootstrapping endpoint ⋮ Endpoint estimation for observations with normal measurement errors ⋮ On dealing with the unknown population minimum in parametric inference ⋮ Estimating an endpoint with high-order moments ⋮ Bias reduction for endpoint estimation ⋮ On the estimation of Pareto fronts from the point of view of copula theory ⋮ Comparing extreme models when the sign of the extreme value index is known ⋮ Empirical likelihood confidence intervals for the endpoint of a distribution function ⋮ Inferring extinction from a sighting record ⋮ Does bias reduction with external estimator of second order parameter work for endpoint? ⋮ Smooth tail-index estimation

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