On two simple and effective procedures for high dimensional classification of general populations

DOI10.1007/s00362-015-0660-8zbMath1336.62187arXiv1501.01763OpenAlexW2076636512MaRDI QIDQ284189

Zhaoyuan Li, Jian-feng Yao

Publication date: 17 May 2016

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1501.01763

zbMATH Keywords

delocalizationdeterminant-based criterionhigh dimensional classificationlarge sample covariance matrixtrace-based criterion

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

On the dimension effect of regularized linear discriminant analysis ⋮ High-dimensional linear models: a random matrix perspective ⋮ Test on the linear combinations of covariance matrices in high-dimensional data

Uses Software

• ascrda

Cites Work

• Unnamed Item
• Unnamed Item
• Fast nonparametric classification based on data depth
• Regularized linear discriminant analysis and its application in microarrays
• Two sample tests for high-dimensional covariance matrices
• Discriminant analysis of multivariate repeated measures data with Kronecker product structured covariance matrices
• Some tests for the covariance matrix with fewer observations than the dimension under non-normality
• Sparse linear discriminant analysis by thresholding for high dimensional data
• Asymptotic expansion of the misclassification probabilities of D- and A- criteria for discrimination from two high dimensional populations using the theory of large dimensional random matrices
• High-dimensional classification using features annealed independence rules
• Spectral analysis of large dimensional random matrices
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Asymptotic probabilities of misclassification of two discriminant functions in cases of high dimensional data
• ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY
• Tests for High-Dimensional Covariance Matrices
• A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant
• Error rates in classification consisting of discrete and continuous variables in the presence of covariates

This page was built for publication: On two simple and effective procedures for high dimensional classification of general populations