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Abstract
Large sample properties of statistics used in latent root regression analysis are investigated by examining the matrix of correlations among the predictor and response variables as the sample size becomes infinite. The latent roots and latent vectors of the asymptotic correlation matrix are derived for specific model configurations of interest. From the study of the asymptotic latent roots and latent vectors, a new statistic is proposed for use in detecting nonpredictive multicollinearities.