A Test for a Specific Principal Component of a Correlation Matrix

In the application of principal components analysis it is common to replace an observed sample principal component vector by another vector closely resembling the sample vector but which is easier to use or interpret. A useful test of hypothesis in this case is one that specifies the true ith principal component. In this article we obtain an asymptotically chi-squared procedure suitable for testing such a hypothesis when the principal components analysis is performed on a correlation matrix. The procedure easily extends to a principal components analysis based on M estimates of scatter.

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