STAT 4003

Statistical Inference

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