Syllabus. Winter 2008. Statistics 201b: Regression Analysis.
Prof. Rick Paik Schoenberg.
Lectures: MW 3:00-4:20pm, MS 5137.

Office hours: Mondays and Wednesdays, 4:20-5:00pm, MS 8965.

email: frederic@stat.ucla.edu

Course webpage: http://www.stat.ucla.edu/~frederic/201b/W08

Required Text: "Elements of Statistical Learning", by T. Hastie, R. Tishbirani, J. Friedman (Springer, 2001).

Suggested reading: "Generalized Linear Models" by McCullagh and Nelder.

Description: Applied regression analysis, with emphasis on general linear model (e.g. multiple regression) and generalized linear model (e.g. logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, point process regression.

Grading:
Class participation (10%).
Homework (20%).
Midterm. (30%).
Final exam. (40%).
The midterm is Monday, Feb 25, 3:00-4:20pm (in class). The final exam is Thursday, March 20, 8-11am. Homeworks will be announced in class.

Homeworks must be handed in at the beginning of class, or may be slipped under my office door (MS 8965) any time before class. Each homework assignment is graded out of 10 points. Homeworks handed in between 5 and 10 minutes after class has begun will be given a one-point deduction. Those handed in between 10 and 20 minutes late will be given a two-point deduction. Homeworks handed in between 20 minutes late and the end of class will be given a three-point deduction. Homeworks submitted after lecture is over will not be accepted. Homeworks must be submitted in hard copy, rather than by email or fax.

Rough Outline:
Week 1: Introductory material, regression overview, nearest neighbors, overview of high-dimensional problems, function approximation, and bias/variance tradeoffs. Chapters 1-2.
Week 2: Linear regression, Gauss-Markov theorem, multiple regression. Chapters 3.1-3.3.
Week 3: Subset selection, shrinkage. Chapters 3.4-3.5.
Week 4: Regression of an indicator matrix, discriminant analysis. Chapters 4.1-4.3.
Week 5: Logistic regression. Chapter 4.4.
Week 6: Kernel regression. Chapter 6.1-6.3.
Week 7: Review and midterm.
Week 8: Generalized Additive Models. Chapter 9.1.
Week 9: Splines, wavelets (Ch. 5), Poisson regression, point process regression (outside readings).
Week 10: Review.