High-Dimension, Low Sample Size Asymptotics of Canonical Correlation Analysis
classification
🧮 math.ST
stat.TH
keywords
correlationanalysiscanonicalconditionssamplesetssizeasymptotic
read the original abstract
An asymptotic behavior of canonical correlation analysis is studied when dimension d grows and the sample size n is fxed. In particular, we are interested in the conditions for which CCA works or fails in the HDLSS situation. This technical report investigates those conditions in a rather simplified setting where there exists one pair of directions in two sets of random variables with non-zero correlation between two sets of scores on them. Proofs and an extensive simulation study supports the findings.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.