140.734.41
Statistical Theory IV

Location
Internet
Term
4th Term
Department
Biostatistics
Credit(s)
4
Academic Year
2023 - 2024
Instruction Method
Synchronous Online
Class Time(s)
M, W, 10:30 - 11:50am
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Elizabeth Ogburn
Contact Name
Elizabeth Ogburn
Frequency Schedule
Every Year
Prerequisite

Linear algebra; matrix algebra; real analysis; calculus; 140.731-33

Description
Focuses on the asymptotic behavior of estimators, tests, and confidence interval procedures. Includes specific topics: 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:
  1. Examine foundational concepts of statistical inference
  2. Give conditions for consistency and asymptotic normality of M-estimators
  3. Determine the asymptotic distribution of M-estimators
  4. Construct tests and confidence regions for parameters of generalized linear models
  5. 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
Consent Contact
eogburn@jhu.edu
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.