The course will focus the using and interpreting advanced statistical methods with applications in a number of different areas. This course is designed for Master and PhD students in Statistics, and is REQUIRED for the Applied paper of the PhD Comprehensive Exams in Statistics. The course is a mixture of theory and applications, and will include a number of projects which will involve computing with R.
Statistical methodology to be covered includes:
Additional topics will include:
Four homework assignments will each account for 15% of the final grade, with a final exam providing the remaining 40%.
23 February 2019 (?)
The course will not adhere closely to any single text, though the following will be referred to frequently.
Students will need to have access to a computer running a recent version of R, and several additional packages for R will be installed. Those not familiar with R are encouraged to become so within the first few weeks of the course.
office hours: Wednesday 9-10, Thursday mornings 10-11
Smoothing and semi-parametric models
Davison, A. (2003). Statistical models. Cambridge University Press.http://books1.scholarsportal.info/viewdoc.html?id=/ebooks/ebooks1/cambridgeonline/2012-11-08/1/9780511815850
Maindonald, J. & Braun, W.J. (2010). Data analysis and graphics using R: An example-based approach (3rd edition). Cambridge: Cambridge University Press.https://www.cambridge.org/core/books/data-analysis-and-graphics-using-r/E04AEC5BCEF09D2E51A63EB5A8CB0680
Wakefield, J. (2013). Bayesian and frequentist regression methods. Springer New York.