Thursday, September 1 Matlab Software - Time Series of Curves - Chemometrics Data - Mortality Data

Tuesday, September 6 Gene Cell Cycle Data - Microarrays and HDLSS visualization - DWD bias adjustment - NCI 60 Data

Thursday, September 8 Finish NCI 60 Data - Linear Algebra Review - Multivariate Probability Review - PCA as an optimization Problem - PCA Mathematics and Graphics

Tuesday, September 13 PCA Redistribution of Energy - PCA Data Representation - Alternate PCA Computation & SVD - Primal - Dual PCA - Connections between discrete and continuous curve data

Thursday, September 15 Finished Primal-Dual PCA vs. SVD - PCA for Corpora Callosa - Fourier Boundary Representation - Medial Representation - Movies for Visualization

Tuesday, September 20 Cornea Data - Robust HDLSS (Spherical) PCA

Thursday, September 22 Out of Town

Tuesday, September 27 Elliptical PCA - Cluster & PCA - Revisit NCI60 Data - Mass Flux Data - SiZer

Thursday, September 29 SiZer - Revisit Mass Flus Data - SiZer Analysis of Cell Cycle Data - Data Representation

Tuesday, October 4 Euclidean data, not near subspace - M-reps - Bladder Prostate Rectum - Data on manifolds - Mildly Non-Euclidean data - Trees as Data - Strongly Non-Euclidean Data

Thursday, October 6 Participant Presentations: Lingsong Zhang, Travis Gaydos, Ja-Yeon Jeong, Marcel Prastawa

Tuesday, October 11 Discrimination - Fisher Linear Discrimination (Nonparametric & Parametric)

Thursday, October 13 Participant Presentations: Martin Styner, Isabelle Corouge, Joshua Stough, Surajit Ray

Tuesday, October 18 Participant Presentations: Myung Hee Lee, Chihoon Lee, Brad Davis, Peter Lorenzen

Thursday, October 20 Fall Break

Tuesday, October 25 Class Cancelled

Thursday, October 27 Participant Presentations: Xuxin Liu, Sushant Rewaskar, Alok Shriram, Abhishek Singh

Tuesday, November 1 Sarang Joshi

Thursday, November 3 Participant Presentations: Josh Levy, Jeongyoun Ahn, Fernando Silva, Christine Xu

Tuesday, November 8 Generalizations of FLD - HDLSS Discrimination - Maximal Data Piling

Thursday, November 10 Participant Presentations: Yufeng Liu, Jiancheng Jiang, Haipeng Shen, Christine Xu

Tuesday, November 15 Participant Presentations: Hua Yang, Dan Samarov, Jie Zhou, Qiong Han

Thursday, November 17 Class Cancelled

Tuesday, November 22 Participant Presentations: Changwon Lin, Xin Fu, Luke Huan, Mihee Lee

Thursday, November 24 Thanksgiving

Tuesday, November 29 Participant Presentations: Xuanyao He, Miao Xie, Fangfang Wang, Ipek Oguz

Thursday, December 1 Embedding and Kernel Spaces - Support Vector Machines - Distance Weighted Discrimination - Revisit micro-array data - Face Data

Tuesday, December 6 Participant Presentations: Vangelis Evangelou, Suman Sen, Ping Bai, Eli Broadhurst

Thursday, December 8 Revisit NCI 60 data - HDLSS Hypothesis Testing: DiProPerm Test - HDLSS Geometric Representation - Independent Component Analysis - ICA for checking Gaussianity

Class Meetings: Tuesday-Thursday 12:30 - 1:45, Smith 107

Taught by: J. S. Marron

Office: Smith 309

Office Hours: Thursday, 2:00 - 3:30

Email: marron@email.unc.edu

Office Telephone: (919) 962-2188

Home Telephone: (919) 493-2844

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Prerequisite is some type of course experience with notions of probability, expectation, variance, covariance, and the multivariate normal distribution, e.g. as in Stat 164 (but there are a number of other courses that will work as well). Most fundamental statistical concepts that are needed (e.g. Principal Component Analysis) will be developed during the course.

Course grading will be done on the basis of student presentations. The presentation will be either about the student's own related work (rather broadly defined), or else about a recent paper in the area.

Enrollment is encouraged, but auditors are also welcome.

- Based only upon an in class presentation
- Could be your work, or I can suggest something

- Will ask auditors to present as well
- Thus, why not enroll in the course?
- Enrolling will increase
frequency with which
courses like this are offered.

- Heartily encouraged
- Dialogs at various levels are enjoyable and useful
- Please jump in with questions of clarity
- Questions like "what are you talking about?" are encouraged
- As are requests of the from "please summarize what
has been discussed"