STAT 2221: Advanced Applied Multivariate Analysis
Spring 2015, Tuesday - Thursday 2:30-3:45 at 218 Cathedral of Learning
Instructor: Sungkyu Jung
- e-mail address:
sungkyu (at) pitt.edu
- Office: CL
2734
- Phone: 412-624-9033
- Office Hours: Tuesday
and Thursday 4 – 4:30 or by appointments
Grader: Ms. Qiyao Wang
- email: QIW31 (at) pitt.edu
News:
- Homework 1, data file, due Thursday January 22, 2015. solution
- Homework 2, due Thursday Feb. 5, 2015. (Problem 2 revised on 2/3/2015, Problem 5 revised on 2/5/2015.) solution
- Homework 3, data files: homeless_smalldata.csv pendigit3.txt pendigit8.txt, due Feb 24, 2015. solution
- Practice Midterm (Revised on 2/24), PCA_Olympic.R
- Homework 4, data files pendigit3.txt pendigit8.txt due Mar 24, 2015. solution
- Homework 5, data files 24psychtests.csv due Apr 9, 2015. solution
- Final Project guidelines
- Proposal due: Thursday, March 26
- Paper due: Thursday, April 16
- Class canceled on Thursday, April 2.
Description:
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.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
- Latent Variable Models - Factor Analysis, slides, Reading: HS Ch. 11, Izenman 15.4
- Clustering, slides, Reading: HS Ch. 12, Izenman 12
- Multidimensional Scaling, slides, Reading: HS 16, Izenman 13
- Classification and Regression Trees, slides, Reading: HS 19.5, Izenman 9
Computing
There is no designated software for this course. You
may use the software that makes the most sense for you. Many
pharmaceutical companies use SAS for compliance with FDA regulations.
Academic intuitions as well as labs often use R and python.
Corporations often use MATLAB, Stata, Minitab, S, etc. because of the
relatively high reliability despite the cost. However, it is expected
that the student immerse herself with use of at least one software.
- SAS
is available on the PCs at all campus computing labs, such as
Cathedral, Posvar, Forbes Quad and Benedum. If in addition you would
like to have SAS on your PC, Pitt's
Software Download Service offers SAS for free. SAS can only
be installed on Windows or Unix environments (No Mac OS).
- R
is a free, open-source software package/programming language for
statistical computing, and is available on the PCs at all
campus
computing labs, such as Cathedral, Posvar, Forbes Quad and
Benedum. If in addition you would like to have R on your
PC/Mac/Unix, it can be downloaded for free at http://www.r-project.org/
- Matlab is available on the PCs at all campus computing labs, such as
Cathedral, Posvar, Forbes Quad and Benedum. If in addition you would
like to have Matlab on your PC, Pitt's
Software Download Service offers it for free.
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.
Elementary
- Johnson, Richard Arnold, and Dean W. Wichern. 2007. Applied
multivariate statistical analysis. Upper Saddle River, N.J: Pearson
Prentice Hall.
- Hardle, Wolfgan g, and Leopold 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
Course Requirements and Grading:
- Homework 55%
- Midterm exam 25% - Thursday, February 26
- Final project 20%
- Lecture attendance and participation 5%
University Policies:
Academic
Integrity
Students in this course will be expected to comply with the
University of Pittsburgh's Policy on Academic Integrity.
Any student suspected of violating this obligation for any reason
during the semester will be required to participate in the procedural
process, initiated at the instructor level, as outlined in the
University Guidelines on Academic Integrity. This may include, but is
not limited to, the confiscation of the examination of any individual
suspected of violating University Policy. Furthermore, no student may
bring any unauthorized materials to an exam, including dictionaries and
programmable calculators.
Disability
Services
If
you have a disability for which you are or may be requesting an
accommodation, you are encouraged to contact both your instructor and Disability
Resources and Services
(DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412)
228-5347 for P3 ASL users, as early as possible in the term.
DRS
will verify your disability and determine reasonable accommodations for
this course.
Copyright
Notice
Course
materials may be protected by copyright. United States copyright law,
17 USC section 101, et seq., in addition to University policy and
procedures, prohibit unauthorized duplication or retransmission of
course materials. See Library
of Congress Copyright Office and the University
Copyright Policy.