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Abstract Details
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Activity Number:
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78
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302502 |
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Title:
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A Novel Error Rate Estimator for the Linear Discriminant Function
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Author(s):
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Peter H. Chen*+ and Dean M. Young
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Companies:
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University of Mary Hardin-Baylor and Baylor University
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Address:
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Box 8420, Belton, TX, 76513,
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Keywords:
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linear discriminant function ;
Monte Carlo simulation ;
conditional error rate ;
Mahalanobis distance
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Abstract:
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We propose a novel estimator of the conditional error rate for the linear discriminant function. The proposed estimator is devised by using the parametric bootstrap method in conjunction with the control variate technique. We demonstrate, via Monte Carlo simulations, that it performs favorably to other currently-available parametric estimators in terms of accuracy and variability under the high-dimensionality and small-sample condition.
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