Skip Navigation

Course Catalog

140.734.01 Statistical Theory IV

4th term
4 credits
Academic Year:
2017 - 2018
East Baltimore
Class Times:
  • M W,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Michael Rosenblum
Course Instructor:
  • Michael Rosenblum

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


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:

Homework (70 %); take-home final exam (30%)

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:

One 1-hour lab per week