Statistical models, likelihoods, maximum likelihood estimators, likelihood ratio, Wald and score tests, confidence intervals, bounds and regions, Bayesian estimation and testing, basic large sample theory, estimating equations, jackknife, bootstrap and permutation.
This course may not be repeated for credit.
Prerequisite(s)
- Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Sections
This course will be offered next in
Winter 2024.