Stat 730, Fall 2014

Stat 730: Multivariate Analysis, Fall 2014

Instructor: Tim Hanson. E-mail: hansont@stat.sc.edu.
Office Hours: 10-11am Tuesday/Thursday, and by appointment.
Office: 219C LeConte College, (803) 777-3859.
Class Meeting Time: Tuesday/Thursday 8:30am to 9:45am LeConte College 201A.
Textbook: Multivariate Analysis, by K.V. Mardia, J.T. Kent, and J.M. Bibby.

Some reference material: Prof. Bethany Wolf's multivariate course at M.U.S.C., Prof. Nick Fieller's multivariate course at the University of Sheffield, Prof. Richard Smith's time series and multivariate course at the University of North Carolina, Prof. Heng Peng's multivariate and data mining course at Hong Kong Baptist University, and Prof. Yiannis Papastathopoulos' multivariate course at the University of Bristol. A nice course on functional data analysis by Prof. Giles Hooker at Cornell.

There is an abundance of excellent course notes and free textbooks available on the web available as supplementary material. Here arethe best that I have found: Smith, Fieller, Marden with R code, Coghlan's book of R code, and Härdle and Simar.

Lecture schedule, notes, and homework

  • Thur., Aug. 21: Course logistics and Chapter 1: Background.
  • Tues., Aug. 26: Chapter 1, continued. William Cleveland's course in data visualiziation with some nice PDF notes.Ranjan Maitra's notes on data visualization; Winnie Chan's notes, and Yan Liu's notes.
  • Thur., Aug. 28: Chapter 2: Random vectors. Homework 1.
  • Tues., Sep. 2: Chapter 2, continued.
  • Thur., Sep. 4: Chapter 3: Normal theory.
  • Tues., Sep. 9: Chapter 3: normal theory continued. Notes on multivariate statistics with R.
  • Thur., Sep. 11: Chapter 3: normal theory continued. Homework 2, due Thursday Sept. 18.
  • Tues., Sep. 16: Chapter 4: Estimation.
  • Thur., Sep. 18: Chapter 4: estimation continued.
  • Tues., Sep. 23: Chapter 5: Hypothesis testing.Homework 3, due Tuesday Sept. 30.
  • Thur., Sep. 25: Chapter 5: hypothesis testing continued. Michail Tsagris' user's manual for his R functions.
  • Tues., Sep. 30: Chapter 5: hypothesis testing continued.
  • Thur., Oct. 2: Chapter 6: Regression.
  • Tues., Oct. 7: Chapter 6: regression continued.
  • Thur., Oct. 9. Chapter 6: regression continued.
  • Tues., Oct. 14. Chapter 12: MANOVA. Homework 4, due Thursday Oct. 23. Rabbit data.
  • Thur., Oct 16. Chapter 12: MANOVA continued.
  • Tues., Oct. 21. Chapter 8: Principal components analysis.
  • Thur., Oct. 23. FALL BREAK! No class.
  • Tues., Oct. 28. Chapter 8: PCA continued.
  • Thur., Oct. 30. Chapter 14: Multidimensional scaling.
  • Tues., Nov. 4. ELECTION DAY. No class.
  • Thur., Nov. 6. PCA and MDS continued.
  • Tues., Nov. 11. MDS continued. Chapter 10: Canonical correlation analysis. PCA and MDS on leisure time data.
  • Thur., Nov. 13. Chapter 13: Cluster analysis.
  • Tues., Nov. 18. Chapter 13: Cluster analysis, continued. More clustering examples. Homework 5.
  • Thur., Nov. 20. Chapter 11: Discriminant analysis.
  • Tues., Nov. 25. Chapter 11: discriminant analysis, continued.
  • Thur., Nov. 27. THANKSGIVING. No class.
  • Tues., Dec. 2. Chapter 9: Factor analysis. Homework 6.
  • Thur., Dec. 4. Odds and ends.