Covariance pooling and stabilization for classification

DOI10.1016/0167-9473(91)90050-CzbMath0850.62472MaRDI QIDQ1896051

Tom Greene, William S. Rayens

Publication date: 17 August 1995

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

empirical Bayesdiscriminant analysiscross-validationmatrix updates

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (9)

Dimensionality reduction in quadratic discriminant analysis ⋮ Adaptive sequential Monte Carlo by means of mixture of experts ⋮ Results in statistical discriminant analysis: A review of the former Soviet Union literature. ⋮ Regularization in discriminant analysis: an overview ⋮ PLS and dimension reduction for classification ⋮ Effect of dimensionality on discrimination ⋮ A comparison of regularization methods applied to the linear discriminant function with high-dimensional microarray data ⋮ The linear and euclidean discriminant functions: a comparison v1a asymptotic expansions and simulation study ⋮ The use of a distance measure in regularised discriminant analysis

Cites Work

• Unnamed Item
• Estimation of a covariance matrix under Stein's loss
• Estimation of the inverse covariance matrix: Random mixtures of the inverse Wishart matrix and the identity
• Empirical Bayes estimation of the multivariate normal covariance matrix
• Multivariate empirical Bayes and estimation of covariance matrices
• Extension of the Gauss-Markov theorem to include the estimation of random effects
• Biased discriminant analysis: Evaluation of the optimum probability of misclassification
• Partially pooled covariance matrix estimation in discriminant analysis
• The application of bias to discriminant analysis
• Shrunken Estimators in Discriminant and Canonical Variate Analysis
• Discriminant Functions When Covariance Matrices are Unequal
• Discriminant Functions when Covariances are Unequal and Sample Sizes are Moderate
• Further applications of bias to discriminant analysis

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