Likelihood ratio test for partial sphericity in high and ultra-high dimensions

Liliana Forzani, Antonella Gieco and Carlos Tolmasky

Journal of Multivariate Analysis, 2017, vol. 159, issue C, 18-38

Abstract: We consider, in the setting of p and n large, sample covariance matrices whose population counterparts follow a spiked population model, i.e., with the exception of the first (largest) few, all the population eigenvalues are equal. We study the asymptotic distribution of the partial maximum likelihood ratio statistic and use it to test for the dimension of the population spike subspace. Furthermore, we extend this to the ultra-high-dimensional case, i.e., p>n. A thorough study of the power of the test gives a correction that allows us to test for the dimension of the population spike subspace even for values of the limit of p/n close to 1, a setting where other approaches have proved to be deficient.

Keywords: Sample covariance matrix; Spiked population model; High-dimensional statistics; Principal component analysis; Random matrix theory (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:159:y:2017:i:c:p:18-38

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DOI: 10.1016/j.jmva.2017.04.001

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