Course Homepage: Spring 2004

Usually Offered: Spring Semesters

Purpose: To acquaint beginning graduate students in statistics and other disciplines with the mathematical development of statistical inference. To provide a foundation for further study in statistical theory at both the master's and doctoral levels.

Current Textbook: Statistical Inference (2nd edn.), by G. Casella and R.L. Berger. Duxbury, 2002.

 

Topics Covered	Chapters	Time
Data Reduction: Likelihood, sufficient statistics, completeness	6.1-6.3	1.5 weeks
Point Estimation: method of moments, maximum likelihood estimation, mean squared error, Cramer-Rao lower bound, best unbiased estimators	7.1-7.2.2, 7.3.1 - 7.3.3	2.5 weeks
Testing Hypotheses: Test statistics, "sensible" tests, most powerful tests and the Neyman-Pearson lemma, UMP tests, monotone likelihood ratio, likelihood ratio tests	8.1 - 8.2.1, 8.3.1 - 8.3.2, 8.3.4	2.5 weeks
Confidence Intervals: pivotal quantities, test inversion, UMA intervals	9.1 - 9.2.2, 9.3.1 - 9.3.2	2 weeks
Asymptotic evaluation: consistency, asymptotic efficiency, large sample inferences	10.1, 10.3 - 10.4	1.5 weeks
Bayesian Methods: prior and posterior distributions, Bayes estimators, Bayesian intervals and tests	7.2.3, 8.2.2, 9.2.4	1.5 weeks
Decision Theory: decision rules, loss function optimality	7.3.4, 8.3.5, 9.3.4	1.5 weeks

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.  

Contact Faculty: Edsel A. Peņa, Xianzheng Huang
(Last Updated: November 28th, 2005)