STAT 851 Generalized Linear Models and ... - People.stat.sfu.ca

STAT 851
Generalized Linear Models and Discrete Data Analysis
Spring, 2012
Lectures: MW 10:30-12:20 (AQ5118)
Instructor: Rachel Altman
Email: rachelm@sfu.ca
Phone: 778.782.3288
Office hours: MW 1-2pm, or by appointment
Location: SC-K10551
Course website: http://people.stat.sfu.ca/∼rachelm/stat851.html
(Some) class notes, data sets, S-PLUS/R code, and assignments will be available on-line. It
is recommended that you print out the notes before each lecture.
Recommended Textbooks:
1. Categorical Data Analysis, 2nd Edition, by Alan Agresti. Publisher: Wiley.
2. Generalized Linear Models, 2nd Edition, by P. McCullagh and J.A. Nelder. Publisher:
CRC Press.
3. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric
Regression Models, by Julian Faraway. Publisher: CRC Press.
If you are intending on buying only one textbook, I would suggest Agresti. I will be following
the outline of that book, more or less. McCullagh and Nelder covers the same material as
Agresti, for the most part, but would be useful if you’re seeking a different perspective or
additional examples. I will be providing substantial sample S-PLUS/R code, but Faraway
might be helpful to those of you who are especially interested in computing.
Computing:
You are welcome to use any software package you’d like to do the data analyses. However,
I will provide support only for S-PLUS/R.
Marking Scheme:
Worth (Tentative) Due Dates
Assignment 1 TBA Jan. 13, 2012
Assignment 2 TBA Jan. 25, 2012
Assignment 3 TBA Feb. 8, 2012
Assignment 4 TBA Feb. 22, 2012
Midterm 20% Mar. 5, 2012 (in class)
Assignment 5 TBA Mar. 20, 2012
Assignment 6 TBA Apr. 5, 2012
Final ∗ 30% Apr. 13, 2012 (9:30am-12:30pm)
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NOTES:
• ∗ You must achieve 50% on the final exam in order to pass the course.
• Time permitting, you will also be expected to make a short, in-class presentation.
Topics Covered (Time Permitting):
1. Introduction to categorical data
2. Review
• Inference in the univariate and bivariate cases
• Linear models, least-squares, matrix notation
• Maximum likelihood theory
3. Theory of generalized linear models
• Model components
– Exponential family and its properties
– Link functions
• Maximum likelihood estimation
– Newton-Raphson method
– Iteratively reweighted least-squares
• Goodness-of-fit
– Analysis of deviance
– Pearson statistic
– Analysis of residuals
• Model selection
4. Particular models
• Binary data
• Categorical data
• Poisson data
• Multinomial data
5. Overdispersion, quasi-likelihood, and estimating equations
6. Random effects: generalized linear mixed models
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Some Final Notes. . .
1. You are expected to be able to communicate statistical concepts both mathematically
and in English. You will be marked on your clarity in both contexts.
2. For assignments, please submit only computer output that is relevant to the solution.
(Most software packages provide much more output than you need!)
3. You may discuss assignment problems with your classmates, but work you hand in
must be your own.
4. The Code of Academic Honesty will be enforced:
http://www.sfu.ca/dean-gradstudies/current/research/academic-honesty.html
5. Please come to office hours or email me (rather than dropping by my office) if you have
questions.
6. In general, late assignments are not accepted except in the case of (documented) illness,
family emergency, etc. If you are having problems finishing an assignment, understanding
the material before an exam, etc., please contact me well in advance.
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