Advanced Statistical Theory I
2022 - 2023
Tu, Th, 1:30 - 2:50pm
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 ObjectivesUpon successfully completing this course, students will be able to:
- Understand large sample theory underlying commonly used statistical procedures such as maximum likelihood, M-estimation, and GMM-estimation.
- Understand the foundations of semi-parametric inference.
- Understand the foundations of the counting process approach to survival analysis.
Final grade applies to all terms