STAT 4003

Description

This course provides an introduction to statistical inference. Topics include statistical models, sampling distributions, asymptotic distributions, sufficiency, maximum likelihood estimation, Bayesian estimation, Rao-Blackwell theorem, Cramér-Rao theorem and the best unbiased estimator, Neyman-Pearson lemma, uniformly most powerful test and general likelihood ratio test.

Learning outcomes 

Upon finishing the course, students are expected to 

• 1. apply basic statistics limit theorems and probability inequalities; 

  2. perform data reduction through various kind of statistical principles; 

  3. derive point estimation, interval estimation and testing procedures, and study their properties;

  4. analyze and evaluate statistical procedures by different criteria, and understand the interpretations of those criteria. 

Academic years

• • 2020 Spring. (CTE-score: 5.95/6)

  • 2019 Spring. (CTE-score: 5.94/6)