This is a topics course on dimension reduction for regression. Nearly all of the material will come from notes and the literature. More information on the course is available in the syllabus.

Instructor: R. Dennis Cook, dennis@stat.umn.edu

Syllabus: The first two assigmnents are included with this syllabus. o/p>

Bibliogaphy: This is a partially annotated bibliography that is frequently updated.

Fisher Lecture: This is a written draft of the 2005 Fisher Lecture. It will form the basis for the first lectures in this course. Reactions, comments, and typos are welcome.

Here are some potentially useful links: SIR references, Adelaide archive of Fisher's papers, Web site for Regression Graphics.

Projects: The class projects suggested here will be updated from time to time.

Some completed Projects (Posted with author's permission):

Adapting PFC Model to Supervised Principal Components by K. Adragni.

R code for three dimension reduction models from the Fisher paper written by G. Chen: PCmodel , PFCmodel-1 , PFCmodel-2

Principal component analysis: a conditional point of view, by L. Forzani.

Asymptotic linearity simulations by B. Shea.

PLS and PIR by X. Chen.