Useful extensions to basic regression, e.g., generalized linear models, mixed effects, smoothing, robust regression, and techniques for dealing with missing data.
By the end of the course, students are expected to be able to:
- Identify appropriate alternatives for problems where a linear regression model should not be used, and discuss the specific difficulties that are being addressed.
- Discuss the advantages and disadvantages of using non-parametric regression methods.
- Fit a mixed effects model when appropriate, and interpret the corresponding parameter estimates.
- Correctly apply robust estimators to determine whether outliers are present in the data, and explain the implications of their removal on subsequent analyses of the data.
- Maronna, Ricardo; Martin, Doug; and Yohai, Victor. Robust Statistics: Theory and Methods. Wiley, 2006.
Note: information on this page is preliminary and subject to change.