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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/101283

    Title: 多變量函數型資料分群分析之研究
    Other Titles: Cluster Analysis for Multivariate Functional Data
    Authors: 李百靈
    Contributors: 淡江大學統計學系
    Keywords: 分群分析;資料深度;多變量函數型資料;Cluster analysis;Data depth;Multivariate functional data
    Date: 2013-08
    Issue Date: 2015-04-21 14:03:02 (UTC+8)
    Abstract: 本計畫主要想探討多變量函數型資料的分群問題,計畫中將討論如何利用 資料深度(data depth)的概念發展新的多變量函數型資料分群演算法。本研究將 討論當以多個變量之資料深度加權數作為距離測度時,如何決定最佳的權數以 達到適當合理的分群結果。此外,由於多變量資料之間的相關性也是重要的資 料特性之一,因此本計畫也將研究多個變量之資料深度間的相關性,期望可提 出不同觀點的分群準則。計畫中所提出的方法將透過模擬研究與實際資料分析 來驗證其執行的正確性與有效性,另外也會比較不同定義之資料深度在分群上 的表現。本計畫期望最後所提出的分群方法可以提供多變量函數型資料另一種 有用可行的方法。
    We aim to study the cluster analysis of multivariate functional data in this project. New multivariate functional clustering criteria via the data depth will be proposed and investigated. One idea of the proposed criteria is to use the weighted data depth of multivariate functions as the distance measure, and the optimal weighting scheme will be the major issue required to be solved. In addition, we also want to investigate the correlation between the data depths of different variables which could be a key point to develop another useful depth-based clustering criterion. The proposed methods will be evaluated through simulation study and data examples. We will also compare the performance of different data depths. We expect that the proposed method can provide another useful tool for cluster analysis of multivariate functional data.
    Appears in Collections:[Graduate Institute & Department of Statistics] Research Paper

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