Business 41912: Applied Multivariate Analysis
Quarter of 2014
Instructor: Ruey S. Tsay
Office: HPC 455
Office hour: (a) Friday: 1:30 pm to 2:30 pm.
(b) By appointment
You may e-mail me questions. E-mail is the easiest way
to make contact with me.
Teaching Assistant: Mr. Yongning Wang, e-mail: email@example.com
Applied Multivariate Statistical Analysis
by R.A. Johnson and D.W. Wichern
6th ed. Prentice Hall, 2007.
Midterm 30% + Final Exam 45%
+ Homework 25%
where total credits of each component are normalized to be 100.
focus of the course: Recent developments in high-dimensional data analysis,
including dimension reduction, Lasso and related sparse regressions, and
independent component analsyis.
Syllabus: of the course.
R is the main package, but students can use any other programs.
Instructions for R will be given. The following R packages are useful for the
course: mvtnorm, fastICA, CCA, lars, gamlr, leaps, rgl (3D plot), mvoutlier
Lecture: (Will be posted weekly before the lectures)
Week1: Review Chapters 1 to 3, and the first half of Chapter 4:
lec1 Data set: T1-2.DAT, Baker.dat, lec2 & Data set: m-ba4c9807.txt, T5-1.DAT, T4-1.DAT
Week2: Random sample from a multivariate normal distribution & Inference about mean
Lec2 (continued) Data set:
Matlab program to obtain Chi-square QQ-plot: qqchi2.m
Matlab program to compute Hotelling T^2: hotelling.m
Matlab program for transformation: boxcox.m
Matlab program to compute various confidence intervals for means of
Matlab programs to handle missing values in a Gaussian random sample:
(a) EM-algorithm: emmiss.m (b) MCMC method: mcmcmiss.m
R package: mvoutlier has some new tools for detecting multivariate outliers
R program to compute Chi-square QQ-plot: qqchi2.R
R program to compute beta-disribution QQ-plot: qqbeta.R
R program to compute statistics for outlier detection: outlier.R
R program to compute Hotelling T^2: Hotelling.R
R program to compute various confidence intervals for means:
Use data: confreg.R, Use summary statistics: confrega.R
R programs for two multivariate control charts: t2chart.R & t2future.R
Week 3: Multivariate Analysis of Variance (MANOVA)
Lec3 Data sets used:T6-1.DAT, T6-2.DAT, t6-9.dat, t6-14.dat, t6-5.dat, t6-6.dat
R programs: contrast.R, Behrens.R , Box_M.R , profile.R, growth.R
R demo: r-manova
Week 4: Multivariate Analysis of Variance (MANOVA) & Linear Regression
Lec4 Data sets used: T7-5.DAT
Week 5: Multivariate Linear Regression & Principal Component Analysis
Lec5: Data set used: T7-4.dat
R example for regression models with time series errors: mlreg-ts
R program for multivariate multiple linear regression analysis: mmlr.R
R commands: princomp
R demonstration: r-pca data: m-pca5c-9003.txt
Week 7: Dimension Reduction: sliced inverse regression, independent components,
and factor models.
Lec7: R script: sir.R Data set used: T8-4.DAT R demonstration: r-factor
Week 8: Canonical correlation analysis and applications.
Lec8 & Data set used: T9-12.DAT & m-pca5c-9003.txt
Week 9: Discriminant analysis and classification, clustering analysis
Lec9 & Lec10
R program: discrim.R (allows for more than 2 populations)
Data sets used: T11-1.DAT, T11-2.DAT t12-3a.dat
Week 10: Hierarchical clustering, multidimensional scaling and visualization of high-dimensional data
Lec11&Data sets used: T12-4.DAT, T12-5.DAT, T12-7m.DAT, T12-9.DAT m-barra-9003.txt
Reading materials for
high dimensional data analysis:
(a) Sliced inverse regression approach:
HW#1: Data sets
HW#2: Data sets used:
HW#3: Data sets used:
HW#4: Data sets used:
HW#5: Data sets used:
(a) Year 2010: Midterm & solutions; final & solutions
(b) Year 2012: Midterm & solutions;
Midterm: Week 6, Friday, May 9
Exam and solutions
Final Exam: Exam week, Friday, June 13, 8:00 am to 11:00 am.
Exam and Solutions