This is a webpage for a course taught in 2013. Here's a link to the lastest, if not current, course webpage.
STAT 2221: Advanced Applied Multivariate Analysis
Fall 2013, Tuesday—Thursday 4-5:15, 236 Cathedral of Learning
Instructor: Sungkyu Jung
- Office: CL 2734 (Office hour by appointment)
- Best contact by email: sungkyu (at) pitt.edu
TA/Grader: Sung Won Lee
- email: sul23 (at) pitt.edu
Course Web page: http://www.stat.pitt.edu/sungkyu/AAMA/
Important statistical methods and relevant theory for analyzing continuous multivariate
data are introduced. The first half of the course examines traditional and fundamental topics
in some depth, and the second half of the course surveys modern topics.
There is no prerequisite for this course. However, I assume students have working knowledge of
probability, statistics and matrix algebra.
Announcement
Thursday, Nov 7. Class cancelled.
Students are invited to Pitt biostat seminar
`Sparse PCA: Concepts, Theory, and Algorithms' by J Lei
Lecture notes / Class Materials
- Multivariate Data Exploration, slides, Reading: HS Ch. 1, Izenman Ch. 4
- Matrix algebra review, notes, Reading: HS Ch. 2, Izenman Sec. 3.2
- Multivariate normal distribution, notes, Reading: HS Ch. 4-5, Izenman Sec. 3.3
- The Wishart distribution, notes, Reading: HS Ch. 4-5, Izenman Sec. 3.4
- Inference for MVN, notes, Reading: HS Ch. 6-7, Izenman Sec. 3.5
- Linear Dimension Reduction: PCA and CCA, slides, Reading: HS Ch. 9-10, 15, Izenman Ch 7
- Classification, slides, Reading: HS Ch. 13, Izenman 8
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Image link to "The Obama-Clinton Divide by Amanda Cox" (NYT)
- Linear Dimension Reduction: Latent Variable Models, slides, Reading: HS Ch. 11, Izenman 15
- Clustering, slides, Reading: HS Ch. 12, Izenman 12
- Multidimensional Scaling, slides, Reading: HS 16, Izenman 13
- MANOVA, notes, Reading:
- Classification II (Support Vector Machine), slides, Reading: Izenman 11
Software
Links to Matlab supplementary files:
Professor Marron's Matlab files
histJ.m
Link to personal LDA and QDA functions in Matlab
As shown in lecture
Homework assignments:
You may discuss the homework problems with fellow students. However, this does not mean that
you can copy each others' solutions. If you do work in a group, all
individuals are required to submit their own homework papers and also to include the names of persons
you have discussed with. There is no penalty in working as a group.
Final exam
Recommended Textbook
Hardle and Simar (2012) and Izenman (2008). You can either download
the books as .pdf files or buy MyCopy Softcover Editions from Springer
Link (at link.springer.com). Each costs about $25.
References: Books with * are reserved in the Engineering library.
Elementary
- *Johnson, Richard Arnold, and Dean W. Wichern. 2007. Applied
multivariate statistical analysis. Upper Saddle River, N.J: Pearson
Prentice Hall.
- Härdle, Wolfgang, and Léopold Simar. 2012. Applied multivariate statistical analysis. Heidelberg: Springer Berlin Heidelberg
(Also visit here for sample codes)
Advanced and applied
- Izenman, Alan Julian. 2008. Modern multivariate statistical
techniques: Regression, classification, and manifold learning. New
York: Springer New York.
- Hastie, Trevor, Robert Tibshirani,
and J. H. Friedman. 2009.The elements of statistical learning: Data
mining, inference, and prediction. New York, NY: Springer New York.
Theoretical
- *Anderson, T. W. 2003. An introduction to multivariate statistical analysis. Hoboken, N.J: Wiley-Interscience.
- *Muirhead, Robb J. 1982. Aspects of multivariate statistical theory. New York: Wiley.
Working with R or SAS
- Everitt, Brian, and Torsten Hothorn. 2011. An introduction to applied multivariate analysis with R. New York: Springer.
- *Khattree, Ravindra, and Dayanand N. Naik. 1999. Applied multivariate statistics with SAS software. Cary, NC: SAS Institute.
- *Khattree, Ravindra, and Dayanand N. Naik. 2000. Multivariate Data
Reduction and Discrimination with SAS Software. Cary, NC: SAS Institute.
- a Little Book of R for Multivariate Analysis