This article introduces a novel approach to integrating correlation matrix information from training samples to construct a classification rule for testing samples. Traditional discriminant analysis methods that rely solely on mean vectors tend to perform poorly when the mean of the training samples is not indicative of the testing samples. To address this limitation, we propose a new discriminant analysis method called Correlation-matrix driven Discriminant Analysis (CorrDA). By considering the correlation matrices of different classes in the training samples, we can capture the unique patterns among the classes. CorrDA utilizes the Bayes classifier and mixture models to effectively incorporate the correlation matrix information derived from the training samples, thereby improving the discriminant analysis performance on the testing data. Through the analysis of COVID-19 datasets and extensive simulation studies, we provide empirical evidence demonstrating the superior performance of CorrDA.

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To cite this article: Feifei Yan, Yingjie Zhang, Jing Ning, Hai Shu & Ziqi Chen (2026) CorrDA: correlation-matrix driven discriminant analysis, Statistical Theory and Related Fields, 10:2, 167-183, DOI: 10.1080/24754269.2026.2652551 To link to this article: https://doi.org/10.1080/24754269.2026.2652551