 On the border of extreme and mild spiked models in the HDLSS frameworkMyung Hee Lee Journal of Multivariate Analysis, 2012, vol. 107, issue C, 162-168 Abstract: In the spiked covariance model for High Dimension Low Sample Size (HDLSS) asymptotics where the dimension tends to infinity while the sample size is fixed, a few largest eigenvalues are assumed to grow as the dimension increases. The rate of growth is crucial as the asymptotic behavior of the sample Principal Component (PC) directions changes dramatically, from consistency to strong inconsistency at the boundary of the extreme and mild spiked covariance models. Yet, the behavior at the boundary spiked model is unexplored. We study the HDLSS asymptotic behavior of the eigenvalues and the eigenvectors of the sample covariance matrix at the boundary spiked model and observe that they show intermediate behavior between the extreme and mild spiked models. Keywords: HDLSS asymptotics; HDLSS geometric representation; Principal Component Analysis; Spiked covariance model; Strongly Inconsistent; Subspace Consistent (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:107:y:2012:i:c:p:162-168 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2012.01.003 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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