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

Abstract Details

Activity Number: 519
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #309104
Title: Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data
Author(s): Jean-Eudes J. Dazard*+ and J. Sunil Rao
Companies: Case Western Reserve University and Case Western Reserve University
Address: 10900 Euclid Avenue, Cleveland, OH, 44106,
Keywords: Bioinformatics ; Inadmissibility ; Regularization ; Shrinkage Estimators ; Normalization ; Variance Stabilization
Abstract:

Among the problems posed by high-dimensional datasets (so called p >> n paradigm) are that variable-specific estimators of variances are not reliable and tests statistics have low powers, both due to a lack of degrees of freedom. In addition, variance is observed to be a function of the mean. We introduce a non-parametric adaptive regularization procedure that uses the information contained in the mean to jointly generate local shrinkage estimators of the mean and variance. Regularized t-like statistics derived from these shrinkage estimators have significant more statistical power than their standard sample counterparts, regular common-value shrinkage estimators, or when the information contained in the sample mean is ignored. These estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data.


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