Statistics 210A: Theoretical Statistics (Fall 2018)

Course Information

• Prof. Will Fithian (Instructor)

  • Evans 301

  • Office Hours: Tu 12:30-1:30, Th 11-12 (or by appointment)

• Xiao Li (GSI)

  • Evans 444

  • Office Hours: M 12-1pm, Th 1-2pm

• Lectures TuTh 9:30-11, Davis 534

• Final Exam Tu Dec 11, 3-6pm, location TBD

• Syllabus

• Announcements, handouts at bCourses

• Piazza discussion site for technical questions (no homework spoilers!)

Materials

Materials from class:

• Aug. 29: demo of exponential tilting

• Sep. 20: demo of Bayesian inference for the Beta-Binomial distribution

• Sep. 27: demo of Bayes, hierarchical Bayes, and James-Stein

Assignments:

• Due Sep 6: Homework 1 (source)

• Due Sep 13: Homework 2 (source)

• Due Sep 20: Homework 3 (source)

• Due Sep 27: Homework 4 (source)

• Due Oct 4: Homework 5 (source)

• Due Oct 11: Homework 6 (source)

• Due Oct 18: Homework 7 (source)

• Due Oct 25: Homework 8 (source)

• Due Nov 1: Homework 9 (source)

• Due Nov 8: Homework 10 (source)

• Due Nov 15: Homework 11 (source)

Content

Stat 210A is Berkeley's introductory Ph.D.-level course on theoretical statistics. It is a fast-paced and demanding course intended to prepare students for research careers in statistics.

Topics:

• Statistical decision theory, frequentist and Bayesian

• Exponential families

• Point estimation

• Hypothesis testing

• Resampling methods

• Estimating equations and maximum likelihood

• Empirical Bayes

• Large-sample theory

• High-dimensional testing

• Multiple testing and selective inference

Prerequisites:

• Linear algebra

• Real analysis

• One year of upper-division probability and statistics

References

All texts are available online from Springer Link.

Main text:

• Keener, Theoretical Statistics: Topics for a Core Course, Springer 2010.

Supplementary texts:

• Lehmann and Casella, Theory of Point Estimation, Springer 1998.

• Lehmann and Romano, Testing Statistical Hypotheses, Springer 2005.

• Candes, Stats 300C Lecture notes, Stanford 2016.

• Unofficial Fall 2017 lecture notes (transcribed by student Sinho Chewi)

Grading

Your final grade is based on:

• Weekly problem sets: 50%

• Final exam: 50%

Lateness policy: Late problem sets will not be accepted, but you will get to drop one grade.

Collaboration policy: For homework, you are welcome to work with each other or consult articles or textbooks online, but

1. You must write up the problem by yourself.

2. You may NOT consult any solutions from previous iterations of this course.

3. If you collaborate or use any resources other than course texts, you must acknowledge your collaborators and the resources you used.

Academic integrity: You are expected to abide by the Berkeley honor code. Violating the collaboration policy, or cheating in any other way, will result in a failing grade for the semester and you will be reported to the University Office of Student Conduct.

Reading Assignments