JaLCDOI 10.18926/fest/11526
フルテキストURL 006_025_034.pdf
著者 山西 芳裕| 田中 豊|
抄録 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.
キーワード Functional data Multivariate data Principal component analysis Eigenvalue Eigenvecotor
出版物タイトル 岡山大学環境理工学部研究報告
発行日 2001-02-28
6巻
1号
開始ページ 25
終了ページ 34
ISSN 1341-9099
言語 English
論文のバージョン publisher
NAID 120002313939