- Instructor: Peter Hoff
- TA: Andy McCormack
- Lecture: WF 12:00-1:15, Old Chemistry 116 and online via Sakai
- Office hours: TT 10:30-11:30 (PH), W 1:30-2:30 (AM)

- Lecture notes, code and data
- Multivariate Analysis (Mardia, Kent and Bibby)
- Matrix-Based Introduction to Multivariate Data Analysis (Adachi)
- Applied Multivariate Statistical Analysis (Hardle and Simar)
- Modern Multivariate Statistical Techniques (Izenman)

- Sakai link

2020-10-21: Ideas for the paper project

2020-10-14: Homework 8 assigned, due Wednesday 2020/10/21.

2020-10-02: Homework 7 assigned, due Friday 2020/10/09.

2020-09-25: Homework 6 assigned, due Friday 2020/10/02.

2020-09-18: Homework 5 assigned, due Friday 2020/09/25.

2020-09-11: Homework 4 assigned, due Friday 2020/09/18.

2020-09-04: Homework 3 assigned, due Friday 2020/09/11.

2020-09-03: Finish reading the SVD notes and start reading the notes on random matrices.

2020-08-28: Homework 2 assigned, due Friday 2020/09/04.

2020-08-21: Homework 1 assigned, due Friday 2020/08/28.

2020-08-21: Read the eigendecomposition notes, p.11-22.

2020-08-19: Read the eigendecomposition notes, p.1-10.

- Unsupervised analysis
- eigendecompositions and PCA
- SVD and two-way data
- Covariance estimation and the multivariate normal model
- Factor analysis

- Supervised analysis
- General linear model
- Classification

- Special topics
- Multilinear models
- Distance-based methods
- Copulas
- ICA
- Clustering

#### Supplementary materials

- The Matrix Cookbook
- Matrix Differential Calculus with Applications in Statistics and Econometrics
- Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model

#### Evaluation