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Course Catalog

140.771.01 Advanced Statistical Theory I


1st term
4 credits
Academic Year:
2017 - 2018
East Baltimore
Class Times:
  • Tu Th,  1:30 - 2:50pm
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Daniel Scharfstein
Course Instructor:

Real Analysis, Measure-Theoretic Probability, Introduction to Statistical Theory I-II


Focuses on drawing large sample inferences about "parameters" in statistical models. Develops asymptotic theory for maximum likelihood estimation, M-estimation, and generalized method of moment (GMM) estimation. Discusses formal techniques for constructing estimators in semi-parametric models. Pays particular attention to models for longitudinal and survival data. Special topics presented by guest lecturers. Involves rigorous mathematical arguments so that familiarity with concepts in advanced calculus, real analysis, and measure theory will be required.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Understand large sample theory underlying commonly used statistical procedures such as maximum likelihood, M-estimation, and GMM-estimation.
  2. Understand the foundations of semi-parametric inference.
  3. Understand the foundations of the counting process approach to survival analysis.
Methods of Assessment:

Over two terms (140.771-772): 4 collaborative homeworks (40%); 2 independent homeworks (40%); 1 final project (20%).

Instructor Consent:

No consent required

Special Comments:

Grade for 140.771 and 772 given at completion of 140.772.