This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #307980 |
Title:
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Impact of Relying on Sample Variance-Covariance Estimate on Prediction Accuracy and Statistical Power of Hypothesis Testing When n << p
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Author(s):
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Peter H. Hu*+ and Yue Wang and Jared Lunceford
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Companies:
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Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc.
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Address:
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351 Sumneytown Pike, North Wales, 19454,
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Keywords:
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shrinkage estimator ;
variance-covariance estimator ;
linear combination ;
high-dimensionality ;
discriminant analysis ;
power analysis
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Abstract:
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Among 3 different variance-covariance estimators, the one using sample covariance shows the worst performance in prediction during a discriminant analysis with a high-dimensionality data. Comparison in the distributions of eigen values of these estimates in a simulation study suggests that their performances depend on how large the sample size n is relative to the feature size p. Comparison among 3 estimates with two real data for the variance of a composite score derived from a pre-specified linear combination of multi-variate data consistently shows that the variance estimate ignoring between-feature correlation performs the best. In a simulation study with similar setting, the sample variance of the composite score performs the worst among the 3 estimators. These results imply other estimators for the covariance matrix than using sample covariance should be considered when n << p.
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