Significance test of clustering under high dimensional setting with applications to cancer data

DOI10.1080/00949655.2018.1518448OpenAlexW2890667553MaRDI QIDQ4960768

Lu Lin, Yunquan Song, Ping Dong

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1518448

zbMATH Keywords

clusterpowerhigh dimensionality\(p\)-valueempirical sizesignificance testcancer genes

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Neyman's truncation test for two-sample means under high dimensional setting

Uses Software

• SigClust

Cites Work

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• Multivariate Poisson interpoint distances
• Significance analysis of high-dimensional, low-sample size partially labeled data
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Multivariate tests of uniformity
• A two-sample test for high-dimensional data with applications to gene-set testing
• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Asymptotic Statistics
• Sparse Fisher's linear discriminant analysis for partially labeled data
• Testing diagonality of high-dimensional covariance matrix under non-normality
• Geometric Representation of High Dimension, Low Sample Size Data

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