Theory of Statistics II

(STATS 300B, Winter 2024)
Resources: Prerequisites:

Course Policies: A detailed overview of course policies (including grading and assignments) can be found in the course syllabus.

Course description

This course is the second in the "Theory of Statistics" sequence for Stanford Statistics PhD students. We will first cover classical results from asymptotic statistics, where the primary goal will be to understand (taking a hard-line mathematical perspective) what makes a good estimator in the large sample limit. Then, we will shift our focus to non-asymptotic high-dimensional statistics, where we will build tools for establishing concentration (a.k.a. confidence intervals) of estimators. Finally, we will briefly discuss algorithmic concerns in the high-dimensional regime.

Lectures and Reading
Below is a preliminary schedule (subject to change), including the readings relevant to each lecture.