Tests for a Multiple-Sample Problem in High Dimensions

DOI10.1080/03610926.2012.661505zbMath1462.62345OpenAlexW1970436340MaRDI QIDQ2815361

Yanfeng Shen, Zheng Yan Lin

Publication date: 28 June 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2012.661505

zbMATH Keywords

asymptotic normalityhypothesis testingvariable selectionhigh dimensionmultiple-sample problem

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Asymptotic properties of parametric tests (62F05)

Related Items (1)

Likelihood ratio tests for elaborate covariance structures and for MANOVA models with elaborate covariance structures -- a review

Cites Work

• Multivariate analysis of variance with fewer observations than the dimension
• Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis
• High-dimensional classification using features annealed independence rules
• A test for the mean vector with fewer observations than the dimension under non-normality
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Some high-dimensional tests for a one-way MANOVA
• A test for the mean vector with fewer observations than the dimension
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• Multivariate Theory for Analyzing High Dimensional Data
• A Significance Test for the Separation of Two Highly Multivariate Small Samples
• A High Dimensional Two Sample Significance Test

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