4.7 Article

Canonical Concordance Correlation Analysis

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Summary: This note examines the approach of obtaining predictors of a random variable given the joint distribution, by maximizing the mapping for a given correlation function. The predictors obtained through this method are infinite and the least-squares predictor is obtained by equating the expectations and variances. A unique predictor, called the maximal agreement predictor, is obtained when the variances are equal. Examples of these predictors are illustrated for three special distributions.

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