Courses | PhD in Biostatistics

3 Credits | Fall Semester
Course Instructors: Fan, Ruzong

This is a course for PhD students with advanced knowledge of probability, statistics, and mathematics. The class covers both advanced probability theory and basic theory of stochastic processes to facilitate research of biostatistics and biomedical sciences. For probability theory, the following topics will be taught: measures, integration, probability, large sample theory of random variables. For stochastic processes, an introduction of martingales and point processes with applications to survival analysis will be taught.

3 Credits | Spring Semester
Course Instructors: Makambi, Kepher

This course takes an advanced approach to statistical inference with emphasis on theory and foundations. Topics covered include UMVUE, variance bounds and information inequalities, U-statistics; Bayes decisions and estimators, invariance, MLE, quasi- and conditional likelihoods, and asymptotic relative efficient estimation; empirical likelihoods, density estimation and semi-parametric methods, M-, L-, R-estimation, jackknife and bootstrap; UMP tests, UMP unbiased and similar tests, UMP invariant tests, likelihood ratio tests, asymptotic tests based on the likelihood, Bayes tests, tests in nonparametric models; asymptotic confidence sets, bootstrap confidence sets and simultaneous confidence intervals.

3 Credits | Fall Semester
Course Instructors: Ahn, Jaeil

The course will cover statistical methods for analyzing non-normally distributed data such as proportion, count, and rate data using generalized linear models. The course will cover topics relating to estimation, inference, deviance, diagnosis using both the frequentist and Bayesian framework. The applications include two-way tables; multi-factor, multivariate-responses, variable selection, repeated measurement experiments.

A cognate area will be defined as an area of specialized study within the PhD degree, in a health-related field. This gives students an opportunity to gain knowledge and expertise in a biomedical research field other than Statistics/ Biostatistics. Students must take a minimum of 6 credits in that cognate area.

Cognate Courses: Biochemistry/ Biotechnology, Cell Biology, Environmental Biology, Epidemiology, Global Health, Global Infectious Diseases, Health Physics & Radiation Protection, Integrative Neuroscience, Interdisciplinary Program in Neuroscience, Neuroscience and Tumor Biology.

Non-Cognate Courses: Biostatistics, Computer Science, Econometrics, Mathematics, Psychometrics and Statistics.

This requirement may be satisfied by completing a Bioinformatics course among the elective courses.

BIST 7660: Deep Learning & Artificial Intelligence
3 Credits | Spring Semester
Course Instructor: Li, James

Students must acquire experience in the planning of experiments, analyzing data, reporting results and establish a collaborative interaction with investigators.

BIST 7665: Statistics, Ethics & Communication
1 Credit | Spring Semester
Course Instructor:  Korostyshevskiy, Valeriy

This requirement is met by completing a course in responsible conduct of research.