A course on the mathematical foundations of statistics, with focus on general principles and finite sample optimality.
Topics include:
Decision theory: loss, risk, admissibility.
Bayes estimation; Minimax estimation.
Principles of data reduction. Sufficiency. Exponential families.
Unbiasedness and equivariance.
Hypothesis testing and confidence intervals.
Class Times and Locations
Monday and Wednesday, 9:30-11:20AM in Sequoia 200
Problem Sessions on Friday, 10:30-11:30AM in Sequoia 200
The midterm will take place on Friday, October 26, 3:30-6:30PM in room 380-380Y.