| Instructor: |
Davar Khoshnevisan (Contact Information | Office Hours) |
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| Time/Place: | MW 3:00-4:20 pm, LCB 225 |
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| Text: | No text. Attendance is important! |
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| 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. |
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| No lectures: | Jan 14, 16 |
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| 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.)
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| Grading: | (1) Three to five theory homework assignments; this is
mostly theoretical work. (2) Three of four applied projects are assigned that are based on 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. |
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