### STAT 832: Multivariate Statistical Analysis

#### Course information

- Instructor: Peter Hoff
- TA: Michael Jauch
- Lecture: MW 10:05-11:20, Perkins LINK 087
- Office hours: TTh 10:30-11:30 Rm 219 (PH), T 2:30-4:30 Rm 211A (MJ)

#### Course materials

- Lecture notes, code and data
- Applied Multivariate Statistical Analysis (Hardle and Simar)
- Modern Multivariate Statistical Techniques (Izenman)
- Sakai link

#### Announcements

2017-04-12: Do the twelfth and final set of exercises, exercises 1,2a and 3 in the multilinear model notes, to be turned in Wednesday 2017-04-19.

2017-04-05: Do the eleventh set of exercises, exercises 1,2,4 and 5 in the multivariate regression notes, to be turned in Wednesday 2017-04-12.

2017-03-29: Do the tenth set of exercises,
exercises 6,7 and 8 in the copula notes,

to be turned in Wednesday 2017-04-05.

2017-03-22: Do the ninth set of exercises to be turned in Wednesday 2017-03-29.

2017-03-01: Do the eighth set of exercises to be turned in Wednesday 2017-03-08.

2017-02-22: Do the seventh set of exercises to be turned in Wednesday 2017-03-01.

2017-02-15: Do the sixth set of exercises to be turned in Wednesday 2017-02-22.

2017-02-08: Do the fifth set of exercises to be turned in Wednesday 2017-02-15.

2017-02-01: Do the fourth set of exercises to be turned in Wednesday 2017-02-08.

2017-01-25: Do the third set of exercises to be turned in Wednesday 2017-02-01.

2017-01-18: Do the second set of exercises to be turned in Wednesday 2017-01-25. The data exercises should be done in Rmarkdown, and an executable file should be submitted via Sakai.

2017-01-11: Do exercises 1 through 7 in the course notes on eigendcompositions, to be turned in Wednesday 2017-01-18.

#### Tentative schedule of topics

- EDA
- Matrix decompositions and linear methods
- Nonlinear methods

- Multivariate normal theory
- General linear model
- Equivariant estimation and testing
- Distributional results for linear methods
- Shrinkage estimation and Bayesian methods

- Tensor data
- Tensor decompositions
- Multilinear regression and covariance models

- Copula models
- Parametric methods
- Semiparametric methods

- Distributions over special manifolds
- Directional data
- Models for eigenvectors

- Big data
- Graphical models and sparse estimation
- Optimal shrinkage when $p\approx n$

#### Evaluation

- 60% Homework
- 20% Data project
- 20% Paper project
- grade = x exp(-l/8), where x=score, l=days late (including weekends)