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Data · collection · 2015

Weighted Statistic in Detecting Faint and Sparse Alternatives for High-dimensional Covariance Matrices

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This paper considers testing equality of two population covariance matrices when the data dimension p diverges with the sample size n ( p / n → c > 0).

Description

We propose a weighted test statistic which is data-driven and powerful in both faint alternatives (many small disturbances) and sparse alternatives (several large disturbances). Its asymptotic null distribution is derived by large random matrix theory without assuming the existence of a limiting cumulative distribution function of the population covariance matrix.

The simulation results confirm that our statistic is powerful against all alternatives, while other tests given in the literature fail in at least one situation.

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Topics

Stated by source
Biological sciences · Mathematics
Inferred from text
Simulation 75%

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Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
DataCite10.6084/m9.figshare.c.2170529.v112 d agoJSON v1
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concepts[disease].local:disease:cancermapping · DataCitevocabulary-mapper@1.0.0keywords['Cancer']
concepts[field].fos:biological-sciencessource · DataCiteconnector:datacite@1.0.0
concepts[field].fos:mathematicssource · DataCiteconnector:datacite@1.0.0
concepts[field].local:field:life-sciencesmapping · DataCitevocabulary-mapper@1.0.0keywords['Biological Sciences']
concepts[field].local:field:mathematics-statisticsmapping · DataCitevocabulary-mapper@1.0.0keywords['Mathematics']
concepts[method].local:method:simulationenrichment · DataCitekeyword-concept-rules@1.0.0title+description (75%)
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