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Estimation of Extreme Quantiles for Functions of Dependent Random Variables
【2014.3.14 4:00pm,S712】

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 2014-3-11 

  Colloquia & Seminars 

  Speaker

  张希承 教授,武汉大学数学与统计学院

  Title

     Estimation of Extreme Quantiles for Functions of Dependent Random Variables            

  Time

  2014.3.14 4:00pm                                        

  Venue

  S712 

  Abstract

 We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contrast to the conventional approach based on extreme value theory, we do not impose the condition that the tail of the underlying distribution admits an approximate parametric form, and, furthermore, our estimation makes use of the full observed data. The proposed method is semiparametric as no parametric forms are assumed on all the marginal distributions. But we select appropriate bivariate copulas to model the joint dependence structure by taking the advantage of the recent development in constructing large dimensional vine copulas. Consequently a sample quantile resulted from a large bootstrap sample drawn from the fitted joint distribution is taken as the estimates for the extreme quantile. This estimator is proved to be consistent as long as the quantile to be estimated is not too extreme. The reliable and robust performance of the proposed method is further illustrated by simulation.

  Affiliation

 

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