Instructor: |
Davar Khoshnevisan (Contact Information | Office Hours) |
Time/Place: | MW 2:00-3:15 pm,
ST214 |
Text: | None. Lecture notes are made available frequently. |
Course Description: |
This is a graduate-level course on mathematical statistics, and is a core requirement for
the professional Master's Degree in Statistics, offered by the Mathematics Department.
The course emphasizes solid understanding of the theoretical underpinnings
of the subject, as well as the application of these ideas and methods to
data analysis in concrete settings. |
Topics: | Topics vary from term to term,
depending on the interests of the instructor.
This semester, we cover some or all of the following topics:
- A primer of probability and distribution theory (2 weeks, approx.)
- A primer of elementary statistics (1 weeks, approx.)
- Empirical processes (2 weeks, approx.)
- Non-parametric methods (2 weeks, approx.)
- Resampling methods: The bootstrap and the jackknife (1 week, approx.)
- Density Estimation (2 weeks, approx.)
- Time series analysis (4 weeks, approx.)
|
Grading: | Homework is assigned fairly regularly.
This is mostly theoretical work. Four or five applied projects are assigned that are
based on small to medium-sized data sets.
These are larger in scope than the theory homework, and the student is expected to produce
complete reports based on his/her findings. The reports must be
typed up according to publication standards of Statistics. (See "Projects" below.) |