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ID 11526
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Sort Key
4
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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
Publisher
岡山大学環境理工学部
Publisher Alternative
Faculty of Environmental Science and Technology, Okayama University
Start Page
25
End Page
34
ISSN
1341-9099
NCID
AN10529213
Content Type
Departmental Bulletin Paper
OAI-PMH Set
岡山大学
language
英語
File Version
publisher
NAID
Eprints Journal Name
fest