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

DOI10.1016/j.jmva.2017.04.001zbMath1368.62148OpenAlexW2606203676MaRDI QIDQ2011514

Liliana Forzani, Carlos Tolmasky, Antonella Gieco

Publication date: 3 August 2017

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2017.04.001

zbMATH Keywords

principal component analysisrandom matrix theorysample covariance matrixhigh-dimensional statisticsspiked population model

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Hypothesis testing in multivariate analysis (62H15)

Related Items

Exploring dimension learning via a penalized probabilistic principal component analysis, Some sphericity tests for high dimensional data based on ratio of the traces of sample covariance matrices

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