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


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

  1. EDA
    1. Matrix decompositions and linear methods
    2. Nonlinear methods
  2. Multivariate normal theory
    1. General linear model
    2. Equivariant estimation and testing
    3. Distributional results for linear methods
    4. Shrinkage estimation and Bayesian methods
  3. Tensor data
    1. Tensor decompositions
    2. Multilinear regression and covariance models
  4. Copula models
    1. Parametric methods
    2. Semiparametric methods
  5. Distributions over special manifolds
    1. Directional data
    2. Models for eigenvectors
  6. Big data
    1. Graphical models and sparse estimation
    2. 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)