JSM 2004 - Toronto

Abstract #300775

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Activity Number: 276
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300775
Title: Robust Dimension Reduction Based on CANCOR
Author(s): Jianhui Zhou*+
Companies: University of Illinois, Urbana-Champaign
Address: Dept. of Statistics, 101 Illini Hall, Champaign, IL, 61820,
Keywords: canonical correlation ; dimension reduction ; MCD estimator ; permutation test ; robustness
Abstract:

The canonical correlation (CANCOR) method for dimension reduction in a regression setting is based on the first and second moments of the data, and therefore sensitive to outliers. We study a weighted canonical correlation (WCANCOR) method where each observation is weighted based on its Mahalanobis distance to the center of the predictors. Robust estimates of the location and scatter such as the minimum covariance determinant (MCD) estimator of Rousseeuw (1984) can be used to compute the Mahalanobis distance. To determine the number of significant dimensions in the WCANCOR method, we consider a weighted permutation test. A comparison between the weighted CANCOR and the weighted SIR methods is also made to see the relative merits of each method.

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