Evidence map›Paper›PMID 33833489›Full record

ArticleStatistica Sinica2021

Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models.

David Morales-Jimenez, Iain M Johnstone, Matthew R McKay, Jeha Yang

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Article in Statistica Sinica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

David Morales-JimenezECIT Institute, Queen's University Belfast, UK.
Iain M JohnstoneDepartment of Statistics, Stanford University, USA.
Matthew R McKayECE Department, Hong Kong University of Science and Technology, Hong Kong.
Jeha YangDepartment of Statistics, Stanford University, USA.

Funding

New Statistical Methods for Medical Signals and ImagesR01EB001988 · NIBIB · STANFORD UNIVERSITY · PI JOHNSTONE, IAIN M · 2003 to 2018
$6.1M
New Statistical Methods for Medical Signals and ImagesR01GM134483 · NIGMS · STANFORD UNIVERSITY · PI Iain M Johnstone · 2019 to 2026
$3.9M
NIBIB NIH HHS R01 EB001988NIGMS NIH HHS R01 GM134483
6 · The paper itself

Abstract

Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply random matrix theory to derive asymptotic first-order and distributional results for both leading eigenvalues and eigenvectors of sample correlation matrices, assuming a high-dimensional regime in which the ratio

Indexed as

eigenstructureSample correlationspiked models

Identifiers

PMID33833489
PMCPMC8026145

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