140.648.41
Essentials of Probability and Statistical Inference III: Theory of Modern Statistical Methods

Prerequisite:

Working knowledge of calculus

Description:

Builds on the concepts discussed in 140.646 and 140.647 to lay out the foundation for both classical and modern theory/methods for drawing statistical inference. Includes classical unbiased estimation, unbiased estimating equations, likelihood and conditional likelihood inference, linear models and generalized linear models, and other extended topics. De-emphasizes mathematical proofs and replaces them with extended discussion of interpretation of results and examples for illustration.

Learning Objectives:

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

1. Identify the likelihood principal and become aware the multiple ways to achieve an estimator that satisfies the likelihood principal. For classic parametric models, be able to derive the maximum likelihood estimator.
2. Comprehend the properties of maximum likelihood estimators for parametric models and use it for statistical inference.
3. Deeply identify the philosophy behind statistical inference within the frequentist framework. Be able to conduct hypothesis testing for some specific context.

Methods of Assessment:

This course is evaluated as follows:

• 50% 4-5 problem sets
• 50% Final Exam

Instructor Consent:

Consent required for some students

Consent Note:

Consent required only for students who have not taken 140.646 and 140.647

For consent, contact:

nzhao10@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 (time TBA)