Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA
نویسندگان
چکیده
منابع مشابه
Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA
In High Dimension, Low Sample Size (HDLSS) data situations, where the dimension d is much larger than the sample size n, principal component analysis (PCA) plays an important role in statistical analysis. Under which conditions does the sample PCA well reflect the population covariance structure? We answer this question in a relevant asymptotic context where d grows and n is fixed, under a gene...
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Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows (i.e. High Dimension, Low Sample Size (HDLSS)) are becoming increasingly relevant. We investigate the asymptotic behavior of the Principal Component (PC) directions. HDLS...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2012
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2012.03.005