A Log Probability Weighted Moment Estimator of Extreme Quantiles
Frederico Caeiro & Dora Prata Gomes – Universidade Nova de Lisboa, FCT and CMA

We consider a new semi-parametric Probability Weighted Moment estimator for extreme quantiles of a right heavy-tail model. Under a second-order regular variation condition on the tail, of the underlying distribution function, we deduce the non degenerate asymptotic behaviour of the estimator and present an asymptotic comparison at their optimal levels. In addition, the performance of the estimator is illustrated through an application to real data.

Frederico Caeiro

Frederico Caeiro is an Auxiliary Professor at the Mathematics Department of the Faculty of Science and Technology – Nova University of Lisbon and a member of the Mathematics and Application Research Center (Portugal). He has an MSc degree in Probability and Statistics (2001) and a PhD degree in Statistics (2006) from Faculty of Science – Lisbon University. His current research interests include Statistics of Extremes, Extreme Value Theory, Nonparametric Statistics and Computational Statistics Methods. To know more…


“Statistical inference about rare events is clearly linked to observations which are extreme. Extreme Value Theory provide us the right tool to study such events and usually provide us more reliable estimates than traditional statistical methods.”

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