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題名 一種基於函數型資料主成分分析的曲線對齊方式
A Curve Alignment Method Based on Functional PCA
作者 林昱航
Lin,Yu-Hang
貢獻者 黃子銘
林昱航
Lin,Yu-Hang
關鍵詞 函數型資料分析
對齊程序
主成分分析
functional data analysis
registration procedures
principal component analysis
日期 2011
上傳時間 30-Oct-2012 10:58:23 (UTC+8)
摘要 函數型資料分析的是一組曲線資料,通常定義域為一段時間範圍。常見的如某一個地區人口在成長期的身高紀錄表或是氣候統計資料。函數型資料主要特色曲線間常有共同趨勢,而且個別曲線反應共同趨勢時也有時間和強度上的差異。本文研究主要是使用Kneip 和 Ramsay提出,結合對齊程序和主成分分析的想法作為模型架構,來分析函數型資料的特性。首先在對齊過程中,使用時間轉換函數(warping function),解決觀測資料上時間的差異;並使用主成分分析方法,幫助研究者探討資料的主要特性。基於函數型資料被預期的共同趨勢性,我們可以利用此一特色作為各種類型資料分類上的依據。此外本研究會對幾種選取主成分個數的方法,進行綜合討論與比較。
In this thesis, a procedure combining curve alignment and functional principal component analysis is studied. The procedure is proposed by Kneip and Ramsay .In functional principal component analysis, if the data curves are roughly linear combinations of k basis curves, then the data curves are expected to be explained well by principle component curves. The goal of this study is to examine whether this property still holds when curves need to be aligned. It is found that, if the aligned data curves can be approximated well by k basis curves, then applying Kneip and Ramsay`s procedure to the unaligned curves gives k principal components that can explain the aligned curves well. Several approaches for selecting the number of principal components are proposed and compared.
參考文獻 [1]陳順宇著. 3th.台北市:華泰書局,2004[民93].
     [2]P.Craven and G.Wahba.Smoothing noisy data with spline functions:estimating the correct degree of smoothing by the method of generalized cross validation.Numerische Mathematik,31:377–403,1979.
     [3]A.Kneip and J.O.Ramsay.Combining registration and fitting for functional models.Journalofthe American Statistical Association,103,issue 483:1155–1165,2008.
     [4]Jostein Lillestol and Fridthj of Ollmar.Introduction and applications to financial electricity contracts.2003.
     [5]Ciprian M.Crainiceanu and A.Jerey Goldsmith.Bayesian functional data analysis using winbugs.Journal of Statistical Software,Volume 32,Issue11,January 2010.
     [6]J.O.Ramsay and Silverman.Applied Functional Data Analysis.NewYork:Springer,2002.
     [7]J.O.Ramsay and Silverman.Applied Functional Data Analysis 2th.NewYork:Springer,2005.
     [8]Georg Gr¨on Roberto Viviani and Manfred Spitzer.Functional principal component analysis of fMRI data human brain mapping.Human Brain Mapping,January 2005.
     [9]Larry L.Schumaker.Spline Functions:Basic Theory.John Wiley&Sons,Inc.,1981.
     [10]Subhash原著;呂金河編譯.臺中市:滄海,2005.
描述 碩士
國立政治大學
統計研究所
99354028
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099354028
資料類型 thesis
dc.contributor.advisor 黃子銘zh_TW
dc.contributor.author (Authors) 林昱航zh_TW
dc.contributor.author (Authors) Lin,Yu-Hangen_US
dc.creator (作者) 林昱航zh_TW
dc.creator (作者) Lin,Yu-Hangen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 10:58:23 (UTC+8)-
dc.date.available 30-Oct-2012 10:58:23 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 10:58:23 (UTC+8)-
dc.identifier (Other Identifiers) G0099354028en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54412-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 99354028zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 函數型資料分析的是一組曲線資料,通常定義域為一段時間範圍。常見的如某一個地區人口在成長期的身高紀錄表或是氣候統計資料。函數型資料主要特色曲線間常有共同趨勢,而且個別曲線反應共同趨勢時也有時間和強度上的差異。本文研究主要是使用Kneip 和 Ramsay提出,結合對齊程序和主成分分析的想法作為模型架構,來分析函數型資料的特性。首先在對齊過程中,使用時間轉換函數(warping function),解決觀測資料上時間的差異;並使用主成分分析方法,幫助研究者探討資料的主要特性。基於函數型資料被預期的共同趨勢性,我們可以利用此一特色作為各種類型資料分類上的依據。此外本研究會對幾種選取主成分個數的方法,進行綜合討論與比較。zh_TW
dc.description.abstract (摘要) In this thesis, a procedure combining curve alignment and functional principal component analysis is studied. The procedure is proposed by Kneip and Ramsay .In functional principal component analysis, if the data curves are roughly linear combinations of k basis curves, then the data curves are expected to be explained well by principle component curves. The goal of this study is to examine whether this property still holds when curves need to be aligned. It is found that, if the aligned data curves can be approximated well by k basis curves, then applying Kneip and Ramsay`s procedure to the unaligned curves gives k principal components that can explain the aligned curves well. Several approaches for selecting the number of principal components are proposed and compared.en_US
dc.description.tableofcontents 1緒論............................................5
     2文獻探討.........................................7
     3研究方法.........................................9
     3.1使用基底.......................................9
     3.2函數型分析的結構................................11
     3.3函數型主成分分析................................13
     3.4模型和演算方法..................................15
     3.5主成分個數的選取方式.............................17
     4 資料分析.......................................20
     4.1模擬資料.......................................20
     4.2實證資料.......................................24
     5 結論與建議.....................................27
     5.1結論..........................................27
     5.2建議..........................................28
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099354028en_US
dc.subject (關鍵詞) 函數型資料分析zh_TW
dc.subject (關鍵詞) 對齊程序zh_TW
dc.subject (關鍵詞) 主成分分析zh_TW
dc.subject (關鍵詞) functional data analysisen_US
dc.subject (關鍵詞) registration proceduresen_US
dc.subject (關鍵詞) principal component analysisen_US
dc.title (題名) 一種基於函數型資料主成分分析的曲線對齊方式zh_TW
dc.title (題名) A Curve Alignment Method Based on Functional PCAen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1]陳順宇著. 3th.台北市:華泰書局,2004[民93].
     [2]P.Craven and G.Wahba.Smoothing noisy data with spline functions:estimating the correct degree of smoothing by the method of generalized cross validation.Numerische Mathematik,31:377–403,1979.
     [3]A.Kneip and J.O.Ramsay.Combining registration and fitting for functional models.Journalofthe American Statistical Association,103,issue 483:1155–1165,2008.
     [4]Jostein Lillestol and Fridthj of Ollmar.Introduction and applications to financial electricity contracts.2003.
     [5]Ciprian M.Crainiceanu and A.Jerey Goldsmith.Bayesian functional data analysis using winbugs.Journal of Statistical Software,Volume 32,Issue11,January 2010.
     [6]J.O.Ramsay and Silverman.Applied Functional Data Analysis.NewYork:Springer,2002.
     [7]J.O.Ramsay and Silverman.Applied Functional Data Analysis 2th.NewYork:Springer,2005.
     [8]Georg Gr¨on Roberto Viviani and Manfred Spitzer.Functional principal component analysis of fMRI data human brain mapping.Human Brain Mapping,January 2005.
     [9]Larry L.Schumaker.Spline Functions:Basic Theory.John Wiley&Sons,Inc.,1981.
     [10]Subhash原著;呂金河編譯.臺中市:滄海,2005.
zh_TW