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研究生:林惠珠
研究生(外文):Hui-Chu Lin
論文名稱:分層資料如何進行因素分析之探討
論文名稱(外文):Stratified Data in Factor Analysis
指導教授:傅瓊瑤傅瓊瑤引用關係
指導教授(外文):Chong-Yau Fu
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
中文關鍵詞:分層資料因素分析胎兒資料脈衝指數
外文關鍵詞:Stratified DataFactor AnalysisFetal DataPulsatility Index
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中文摘要
因素分析 (Factor Analysis) 在探索資料方面常扮演著極重要的角色,主要目的是對多個且彼此間具有相關性的變數作歸類。透過線性代數的理論方法,轉換資料的共變異矩陣 (Covariance Matrix),取得解釋變異最大的數個因子 (Common Factors) 達到降低資料維度(Dimension Reduction)的目的。
ㄧ般資料透過適當的分層處理,可減少分層變項 (Stratified Variable) 帶給資料的變異。當分層資料進行統計分析時,技巧上需有適當修正,本研究對統計上的因素分析(Factor Analysis) 提出組平均校正法(Group-mean-corrected method) ,及聯合共變異矩陣法 (Pooled-amount-variation method)二種簡易的修正方法以處理分層資料。
本研究所提出的胎兒資料(Fetal Data),為數個不同部位之血流脈衝指數(Pulsatility Index),利用分層技巧控制懷孕週數所造成的偏差(Bias),再透過因素分析得到解釋變異最大的倆個共同因子。與二元反應變項(出生胎兒正常或不正常)連結時,應用邏輯斯迴歸分析 (Logistic Regression Analysis)及迴歸分類樹(CART) 分析進行二元區分。區分結果顯示,當資料與懷孕週數有高度相關時,修正的資料有較低的錯歸率 (misclassification rate)。
關鍵詞:分層資料(Stratified Data)、因素分析(Factor Analysis)、胎兒資料(Fetal Data)、脈衝指數(Pulsatility Index)。
Abstract
Factor Analysis is one method of exploring the data structure. The main point is to classify correlated variables to reduce the dimension of data. Based on covariance matrix, factor analysis extracts common factors and remains most of variance through matrix algebra.
In practice, stratified data can reduce the additional variance due to the extra variable. In modeling, Conditional logistic regression model, and Stratified PH model are available for stratified data. Factor analysis for stratified data is somehow complicated and not available in software. In this thesis, two methods, pooled-amount-variation and group-mean-corrected methods, are proposed for data correction before factor analysis used.
In this fetal data, Pulsality Index (PI) values are measured from several parts of vessels. The effect caused from gestational age effect is reduced by corrected methods. Two factors remaining most of variation are generated from factor analysis. Combining the binary fetal outcome (normal or abnormal), logistic regression analysis and CART analysis are used for binary discrimination. The discrimination result has lower misclassification rate from corrected data when data highly correlate with gestational age.
Keywords:Stratified Data、Factor Analysis、 Fetal Data、Pulsatility Index﹒
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