140.734.41
Statistical Theory IV
- Location:
- Internet
- Term:
- 4th term
- Department:
- Biostatistics
- Credits:
- 4 credits
- Academic Year:
- 2022 - 2023
- Instruction Method:
- Synchronous Online
- Class Times:
-
- M W, 1:30 - 2:50pm
- Auditors Allowed:
- No
- Undergrads Allowed:
- No
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructor:
- Contact:
- Elizabeth Ogburn
- Resources:
- Prerequisite:
Linear algebra; matrix algebra; real analysis; calculus; 140.731-33
- Description:
-
Focuses on the asymptotic behavior of estimators, tests, and confidence interval procedures. Specific topics include: M-estimators; consistency and asymptotic normality of estimators; influence functions; large-sample tests and confidence regions; nonparametric bootstrap
- Learning Objectives:
-
Upon successfully completing this course, students will be able to:
- Give conditions for consistency and asymptotic normality of M-estimators
- Determine the asymptotic distribution of M-estimators
- Construct tests and confidence regions for parameters of generalized linear models
- Determine when the nonparametric bootstrap is appropriate, and apply it in such cases
- Methods of Assessment:
This course is evaluated as follows:
- 70% Homework
- 30% Take-home final exam
- Instructor Consent:
Consent required for some students
- Consent Note:
Consent required for any students who are not in the Biostatistics PhD program
- For consent, contact:
- Special Comments:
Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register. One 1-hour lab per week.