PCA consistency for the power spiked model in high-dimensional settings
type: Item
instance of: scholarly article
title: PCA consistency for the power spiked model in high-dimensional settings
title: High-dimensional quadratic classifiers in non-sparse settings
zbMATH Open document ID: 1280.62072
zbMATH Open document ID: 1434.62109
author: Kazuyoshi Yata
author: Makoto Aoshima
published in: Journal of Multivariate Analysis
published in: Methodology and Computing in Applied Probability
publication date: 2014-01-13T00:00:00Z
publication date: 2020-01-13T00:00:00Z
full work available at URL: https://arxiv.org/abs/1503.04549
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 34L20
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62H10
zbMATH DE Number: 6244551
zbMATH DE Number: 7150208
zbMATH Keywords: cross-data-matrix methodology
zbMATH Keywords: HDLSS
zbMATH Keywords: large p small n
zbMATH Keywords: microarray data
zbMATH Keywords: noise-reduction methodology
zbMATH Keywords: asymptotic normality
zbMATH Keywords: Bayes error rate
zbMATH Keywords: feature selection
zbMATH Keywords: heterogeneity
zbMATH Keywords: large \(p\) small \(n\)
MaRDI profile type: Publication
OpenAlex ID: W1969057389
OpenAlex ID: W2963599238
arXiv ID: 1503.04549
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Wikidata QID: Q129643956
DOI: 10.1016/J.JMVA.2013.08.003
DOI: 10.1007/S11009-018-9646-Z
description: scientific article; zbMATH DE number 7150208
alternative label: High-dimensional quadratic classifiers in non-sparse settings