Statistics 210A: Theoretical Statistics (Fall 2023)If you are an undergraduate who wants to take the course, please fill out the permission code request form to let me know about your background. Anyone considering taking the course is encouraged to read the frequently asked questions regarding preparation and review materials. Course contentThis is an introductory Ph.D.level course in theoretical statistics. It is a fastpaced and demanding course intended to prepare students for research careers in statistics. Statistics is the study of methods that use data to understand the world. Statistical methods are used throughout the natural and social sciences, in machine learning and artificial intelligence, and in engineering. Despite the ubiquitous use of statistics, its practitioners are perpetually accused of not actually understanding what they are doing. Statistics theory is, broadly speaking, about trying to understand what we are doing when we use statistical methods. See the course introduction for a more detailed explanation as well as comparisons to other Berkeley courses like Stat 215A and B, Stat 210B, and CS 281A/Stat 241A (Statistical Learning Theory). Topics include: Statistical decision theory (frequentist and Bayesian), exponential families, point estimation, hypothesis testing, resampling methods, estimating equations and maximum likelihood, empirical Bayes, largesample theory, highdimensional testing, multiple testing and selective inference. Course Information
MaterialsHandwritten lecture notes (Fall 2023):
Typed lecture notes with additional detail (Fall 2023): Materials from class: Assignments:
ReferencesAll texts are available online from Springer Link. Main text: Supplementary texts:
Undergradlevel review texts for prerequisites:
GradingYour final grade is based on:
Lateness policy: Homework must be submitted to Gradescope at midnight on Wednesday nights. Late problem sets will not be accepted, but we will drop your lowest two grades. Collaboration policy: For homework, you are welcome to work with each other or consult articles or textbooks online, with the following caveats:
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. AccommodationsStudents with disabilities: Please see me as soon as possible if you need particular accommodations, and we will work out the necessary arrangements. Scheduling conflicts: Please notify me in writing by the second week of the term about any known or potential extracurricular conflicts (such as religious observances, graduate or medical school interviews, or team activities). I will try my best to help you with making accommodations, but cannot promise them in all cases. In the event there is no mutuallyworkable solution, you may be dropped from the class. Lecture schedule
