Theory of Statistics I
Lecture
Main Campus
Course Description
Finite sample optimality of statistical procedures; Decision theory: loss, risk, admissibility; Principles of data reduction: sufficiency, ancillarity, completeness; Statistical models: exponential families, group families, nonparametric families; Point estimation: optimal unbiased and equivariant estimation, Bayes estimation, minimax estimation; Hypothesis testing and confidence intervals: uniformly most powerful tests, uniformly most accurate confidence intervals, optimal unbiased and invariant tests. Prerequisites: undergraduate probability (at the level of STATS 118 or MATH 151 or MATH 136). Recommended: STATS 200.
STATS 300A
Units: 3
See When It's Offered opens a new window opens in a new tab
Typically Offered
Autumn
Grading Basis
Letter or Credit/No Credit
Class Level
Graduate
Instructional Mode
In Person
Gen Ed Requirement(s)
None
