Linear discrimination for three-level multivariate data with separable additive mean vector and doubly exchangeable covariance structure

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 15 October 2011 Ricardo Leiva, Anuradha Roy In this article we study a new linear discriminant function for three-level-variate observations under the assumption of multivariate normality. We assume that the-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 15 October 2011 Ricardo Leiva, Anuradha Roy In this article we study a new linear discriminant function for three-level-variate observations under the assumption of multivariate normality. We assume that the-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario

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Linear discrimination for three-level multivariate data with separable additive mean vector and doubly exchangeable covariance structure

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