Statistics 3141: Generalized Additive Models forCross-sectional and Longitudinal Data
Tue-Thu 1:00-2:20PM
Cathedral of Learning 204
Instructor: Prof. Wes Thompson
TEXTBOOKS:
The required textbook will be Simon Wood: GeneralizedAdditive Models, An Introduction with R. Other references used during theclass will be David Ruppert, M.P. Wand, and R.J. Carroll: SemiparametricRegression and R.O. Ramsay and B.W. Silverman Functional Data Analysis. I have these books and you are welcome to borrow them during the semester.
SOFTWARE:
The only required software package will be the R statisticalpackage. This is downloadable for free from
GRADING:
The grading will be as follows:
(i)
(ii)
(iii)
(iv)
(v)
Homework will be assigned and due every two weeks. Thesewill consist of theoretical problems and elementary analyses of data using theR statistical package.
A midterm will be given at roughly the halfway point of theclass and will cover linear models, generalized linear models, and generalizedadditive models. The final exam will be given at the end of class and willcover linear mixed models, generalized linear mixed models, and functional dataanalysis.
The data analysis project will be accomplished within teams consistingof 3 students each. I will provide access to several datasets, or you can useother datasets of your choice (with my approval). You will be expected toanalyze these data using the R statistical package and implementing techniquescovered in class.
The in-class presentations will be given within teams of 2students each, and will consist of a 25 minute presentation of a research paperrelevant to the material covered in this class. These will be graded onclarity of presentation and depth of understanding of the material.
MATERIALS COVERED:
(i) Review of Linear Models & Generalized Linear Models (1/04-2/01)
(ii) Generalized Additive Models (2/06-2/22)
(iii) Linear Mixed Models and Generalized Linear Mixed Models(3/01-3/27)
(iv) Functional Data Analysis (3/29-4/24)