BibTeX
@MISC{Meyer_diagnosticsfor,
author = {Denny Meyer},
title = {Diagnostics for Canonical Correlation},
year = {}
}
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Abstract
Canonical correlation analysis is a versatile multivarite technique that is prone to distortion as a result of correlation outliers. The detection and treatment of such outliers is complicated by outlier masking effects. Methods that check the effect of one observation at a time are therefore unsuccessful as diagnostic tools. In this paper we suggest that an approach involving the robust estimation of correlation matrices be used for canonical correlation analysis, with the robustness weights used to identify outliers. We apply this approach to the full correlation matrix before performing a canonical correlation analysis, and then we apply this approach to the canonical variate scores after performing an initial canonical correlation analysis. Real and simulated examples suggest that, provided an appropriate weighting system is chose, the last approach produces the best performance in terms of detecting canonical correlation outliers and containing them.
Keyphrases
canonical correlation analysis canonical correlation correlation matrix canonical correlation outlier full correlation matrix diagnostic tool appropriate weighting system simulated example canonical variate score masking effect correlation outlier versatile multivarite technique last approach initial canonical correlation analysis robustness weight robust estimation
