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S ta tis tic s of E x tre m e W in d S p e e d s and W a v e H e ig h ts by th e B iv a ria te ACER M e th o dIn the reliability engineering and design o f offshore structures, probabilistic approaches are frequently adopted. They require the estimation o f extreme quantiles o f oceano graphic data based on the statistical information. Due to strong correlation betvi'een such random variables as, eg., wave heights and wind speeds (WS), application o f the multi variate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension o f the average conditional exceedance rate (ACER) method fo r prediction o f extreme value statistics to the case o f bivariate time series. Using the ACER method, it is possible to provide an accurate estimate o f the extreme value distribution o f a univariate time series. This is obtained by introducing a cascade o f conditioning approximations to the true extreme value distribution. When it has been ascertained that this cascade has converged, an estimate o f the extreme value distribution has been obtained. In this paper, it will be shown how the univariate ACER method can be extended in a natural way to also cover the case o f bivariate data. Application o f the bivariate ACER method will be demonstrated fo r measured coupled WS and wave height data.

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