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140.734.01
Statistical Theory IV

Location:
East Baltimore
Term:
4th term
Department:
Biostatistics
Credits:
4 credits
Academic Year:
2022 - 2023
Instruction Method:
In-person
Class Times:
  • M W,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Michael Rosenblum
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:

  1. Give conditions for consistency and asymptotic normality of M-estimators
  2. Determine the asymptotic distribution of M-estimators
  3. Construct tests and confidence regions for parameters of generalized linear models
  4. 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:

mrosenbl@jhsph.edu

Special Comments:

Please note: This is the in-person section of a course that is also offered virtually/online. Students will need to commit to the modality for which they register. One 1-hour lab per week.