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.

Thursday, Nov 7. Class cancelled. Students are invited to Pitt biostat seminar `Sparse PCA: Concepts, Theory, and Algorithms' by J Lei

- 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
- 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

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

- hidalgostamp.R
- kde_examples.m
- swissbanknotes.m, swissbank.mat.zip
- golub.m, Golub.mat.zip
- PCA_EXAMPLE2.R
- CCA_genes.R
- LDAexamples.R
- IRISexample.m
- class_supp.m
- Factor_24psych.R
- 24psychtests.csv
- CocktailParty.m
- MANOVA_iris.R
- svmexample.R

- hw 1. Due on Thursday September 26th. (Assume MVN for problem #7.) HW1_notefromTA.pdf
- hw 2. Due on Thursday October 31st. pendigit3 pendigit8, HW2_notefromTA.pdf
- hw 3. Due on Tuesday December 3rd. 24psychtests.csv

- Take-home, starting at 5PM, Thursday December 5, 2013
- Return by noon, Tuesday December 10, 2013
- Data: S.csv, paclimate.csv
- The PA climate data was obtained from http://www.ncdc.noaa.gov/cdo-web/, and is pre-processed for the purpose of examination.

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.

- *Johnson, Richard Arnold, and Dean W. Wichern. 2007. Applied multivariate statistical analysis. Upper Saddle River, N.J: Pearson Prentice Hall.
- HÃ¤rdle, Wolfgang, and LÃ©opold Simar. 2012. Applied multivariate statistical analysis. Heidelberg: Springer Berlin Heidelberg (Also visit here for sample codes)

- 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.

- *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.

- 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