A variable selection criterion for linear discriminant rule and its optimality in high dimensional and large sample data

DOI10.1016/j.jmva.2013.10.005zbMath1278.62096OpenAlexW1968836412MaRDI QIDQ391949

Masashi Hyodo, Tatsuya Kubokawa

Publication date: 13 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.10.005

zbMATH Keywords

multivariate normalitysecond-order approximationsasymptotic optimalitymisclassification errorlarge sampleshigh dimensionslinear discriminant analysis

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

High-dimensional asymptotic results for EPMCs of W- and Z- rules, Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional settings, Computable error bounds for asymptotic approximations of the quadratic discriminant function, Consistency of test-based method for selection of variables in high-dimensional two-group discriminant analysis

Cites Work

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• Sparse linear discriminant analysis by thresholding for high dimensional data
• Selection of variables in two-group discriminant analysis by error rate and Akaike's information criteria
• Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
• Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large
• Asymptotic expansion and estimation of EPMC for linear classification rules in high dimension
• Variable Selection in High-Dimensional Multivariate Binary Data with Application to the Analysis of Microbial Community DNA Fingerprints
• A Criterion for Selecting Variables for the Linear Discriminant Function
• Linear Statistical Inference and its Applications
• TESTS OF SIGNIFICANCE IN MULTIVARIATE ANALYSIS
• Multivariate Statistics