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Abstract Details
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Activity Number:
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462
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304393 |
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Title:
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Bootstrap Variance Estimator for Weighted Samples Quantiles
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Author(s):
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Xuan Yang*+ and Jingchen Liu
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Companies:
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Columbia University and Columbia University
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Address:
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1255 Amsterdam Avenue, New York, NY, , United States
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Keywords:
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Quantile ;
Bootstrap ;
Importance sampling ;
Asymptotics
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Abstract:
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Quantile estimation is of interest in many areas. When it comes to extreme quantile, the classical sample quantile estimator suffered a problem of large relative variance. Importance sampling could be applied to generate more weighted samples from the neighborhood of the quantile of interest which in turn could help to reduce the estimation error. To evaluate the variance of the weighted sample quantile, we propose a bootstrap variance estimator. We have proved its consistency and derived its convergence rate when sample size goes to infinity. Simulation results are discussed.
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