BIOS 906: Statistical Inference, Fall 2017 — Fall 2019
This is a PHD level inference course, required for all the PHD students. It discusses both point estimation and hypothesis testing. For the point estimation part, this course is focused on loss function, risk function, optimal estimators under various criteria (UMRU, Pitman estimator, Bayes rule, minimax, etc.), admissibility, large sample asymptotic and their related theorems and algorithms (such as EM algorithm). For the hypothesis testing part, the emphasis is put on power and optimality in hypothesis testing (UMP and UMPU test, etc.), and the relationship between confidence regions and hypothesis testing.
BIOS 707: Statistical Methods for Learning and Discovery, Fall 2014 — Fall 2016
This course is a required course for the data mining track MB students. It introduces commonly-used methods in supervised and unsupervised learning, real data application, coding in R, and scientific plotting.
BIOS 704: Introduction to Statistical Theory and Methods II, Spring 2015
This course is a required course for all MB students. Topics include commonly used distributions, maximum likelihood estimators, method of moment estimators, moment generating functions, Central Limit Theorem, Law of Large Numbers, convergence types, sufficient statistics, confidence intervals, and basic concepts in hypothesis testing.