This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #307980
Title: Impact of Relying on Sample Variance-Covariance Estimate on Prediction Accuracy and Statistical Power of Hypothesis Testing When n << p
Author(s): Peter H. Hu*+ and Yue Wang and Jared Lunceford
Companies: Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc.
Address: 351 Sumneytown Pike, North Wales, 19454,
Keywords: shrinkage estimator ; variance-covariance estimator ; linear combination ; high-dimensionality ; discriminant analysis ; power analysis

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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