Abstract
This article addresses the problem of modeling extreme wind speeds with the aim of developing procedures that can be used to reliably identify outliers. There are several approaches to fitting extremes, including using maxima over a fixed time period or taking all observations over a threshold. Using two sets of oceanic wind data from buoys, we use robust estimation methods to estimate the parameters of the asymptotic distribution for extremes over fixed time periods and peaks over threshold. For both cases we also use a gh distribution which focuses on modeling the quantiles and propose a robust method for fitting the data to the gh distribution. Weights from the robust fits are used to identify outliers with P values being computed by resampling. We also evaluate the fits of the data to the model distributions according to several criteria concluding that the gh distribution is at least as effective in fitting the tail behavior as the more classical generalized extreme value distribution and the generalized Pareto distribution.
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