STAT 453 - Theory of Statistics and Data Science (3)

This course covers parametric statistical models, estimation, inference and optimality; sufficiency and related results; details discussion on the exponential family of distributions; the likelihood; detailed discussion on the MLE and its properties including its asymptotic distribution; the delta-method; large sample theory; hypothesis testing, Neyman-Pearson theorem, p-values, UMP and UMPU; LRT and its properties including asymptotics; Bayesian inference including discussions on computations; introduction to statistical decision theory including minimaxity and Bayes rules; the empirical distribution function and statistical functionals; the bootstrap.

Grading: Graded/Satisfactory Unsatisfactory/Audit
Course ID: 57066
Consent: No Special Consent Required
Components: Lecture
Prerequisite: STAT 332 and STAT 451 with a grade of ‘C’ or better.