513—Theory of Statistical Inference. (3) (Prereq: STAT 512 with a grade of C or higher) Hypothesis testing, Neyman-Pearson lemma, likelihood ratio tests, power, theory of linear models including multiple linear regression and ANOVA, Bayesian inferences, advanced topics including survival analysis.
Usually Offered: Fall Semesters
Purpose: To provide a strong foundation in mathematical development of statistical inference methodology.
Current Textbook: Mathematical Statistics with Applications (7th Ed.), D. Wackerly, W. Mendenhall and R. Sheaffer, Duxbury, 2008.
|Hypothesis testing: Type I/II Error, large-sample tests, power, Neyman- Pearson Lemma, uniformly most powerful tests, likelihood ratio tests.||
|Regression models: Simple and multiple linear regression models, least squares, sampling distributions, analysis of variance, F tests, confidence and prediction intervals.||
|Bayesian inference: Bayesian paradigm, prior model selection, posterior computation, point estimation, credible intervals.||
|Survival analysis: Censoring, hazard functions, life-table estimates, Kaplan-Meier estimator, two-sample (log-rank) tests, power and sample size, k-sample tests.||
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: Joshua Tebbs