學習多變量降維方法於統計實務的應用分析。本課程介紹多個創新的統計降維方法(例如:分段逆迴歸法(Sliced Inverse Regression)、主要黑森定向法(principal Hessian Directions)等)，用以達到資料縮減(data reduction)的目的，並且應用至大量資料集。
A Tentative Course Outline :
Week 1 : 迴歸分析之維度縮減；主成份分析 (PCA)
Week 2-3 : 分段逆迴歸法 (SIR)
Week 4-5 : 設限迴歸 (censored regression)
Week 6-7 : 主要黑森定向法 (PHD)
Week 8 : 多變項反應變數迴歸：Most Predictable Variables
Week 9 : 期中報告
Week 10-11 : 泛函資料分析 (functional data analysis)
Week 12-13 : 大p小n之變數選取
Week 14: 最小平均變異估計法：MAVE
Week 15-17: 其他多變量方法: LASSO
Week 18 : 期末報告Learn the application analysis of the multi-variable reduction method in statistical practice. This course introduces several innovative statistical reduction methods (such as Sliced Inverse Regression, principal Hessian Directions, etc.) to achieve the purpose of data reduction and apply to a large number of data sets.
A Tentative Course Outline:
Week 1: Dimension reduction of resection analysis; principal component analysis (PCA)
Week 2-3: Segmented Inverse Reversal Method (SIR)
Week 4-5: Censored regression
Week 6-7: Main Hessian Directional Method (PHD)
Week 8: Multiple variable reaction variables: Most Predictable Variables
Week 9: Midterm Report
Week 10-11: Functional data analysis
Week 12-13: Select the variable number of large p small n
Week 14: Minimum average variation estimation method: MAVE
Week 15-17: Other multivariate methods: LASSO
Week 18: Final Report
No textbook. Lecture notes and selected papers will be available.
No textbook. Lecture notes and selected papers will be available.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業作業 Action |
30 | |
| 學期研究報告(Term paper)學期研究報告(Term paper) Term paper |
40 | 大型資料分析或程式建立(software development) |
| 論文選讀報告論文選讀報告 Reviews and selections for reading reports |
30 |
