Estimating the number of signals using principal component analysis

In this work, we develop inferential tools for determining the correct number of principal components under a general noisy latent variable model, which includes as a special case, for example, the noisy independent component model. The problem is approached using hypothesis testing, and we provide...

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Bibliographic Details
Published inStat (International Statistical Institute) Vol. 8; no. 1
Main Authors Virta, Joni, Nordhausen, Klaus
Format Journal Article
LanguageEnglish
Published The Hague Wiley Subscription Services, Inc 2019
Subjects
bootstrap
Computer simulation
covariance matrix
dimension reduction
Hypothesis testing
independent component analysis
order determination
Principal components analysis
Resampling
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Summary:In this work, we develop inferential tools for determining the correct number of principal components under a general noisy latent variable model, which includes as a special case, for example, the noisy independent component model. The problem is approached using hypothesis testing, and we provide both a large‐sample test and several resampling‐based alternatives. Simulations and an application to sound data reveal that both types of approaches keep the desired levels and have good power.
ISSN:2049-1573
2049-1573
DOI:10.1002/sta4.231