JaLCDOI 10.18926/fest/11526
FullText URL 006_025_034.pdf
Author Yamanishi, Yoshihiro| Tanaka, Yutaka|
Abstract In functional principal component analysis (PCA), we treat the data that consist of functions not of vectors (Ramsay and Silverman, 1997). It is an attractive methodology, because we often meet the cases where we wish to apply PCA to such data. But, to make this method widely useful, it is desirable to study advantages and disadvantages in actual applications. As alternatives to functional PCA, we may consider multivariate PCA applied to 1) original observation data, 2) sampled functional data with appropriate intervals, and 3) coefficients of basis function expansion. Theoretical and numerical comparison is made among ordinary functional PCA, penalized functional PCA and the above three multivariate PCA.
Keywords Functional data Multivariate data Principal component analysis Eigenvalue Eigenvecotor
Publication Title 岡山大学環境理工学部研究報告
Published Date 2001-02-28
Volume volume6
Issue issue1
Start Page 25
End Page 34
ISSN 1341-9099
language 英語
File Version publisher
NAID 120002313939