Bei Jiang, PhD
Contact
Assistant Professor, Faculty of Science - Mathematics & Statistical Sciences
- bei1@ualberta.ca
Overview
Research
Research Areas
Methods for Joint Modeling of Longitudinal and Health Outcome Data, Bayesian Hierarchical Modeling, Mixture Modeling, Functional and Imaging Data Analysis, Kernel Machine Regression/Classification, Bayesian Support Vector Machine.
Courses
STAT 566 - Methods of Statistical Inference
An introduction to the theory of statistical inference. Topics to include exponential families and general linear models, likelihood, sufficiency, ancillarity, interval and point estimation, asymptotic approximations. Optional topics as time allows, may include Bayesian methods, Robustness, resampling techniques. This course is intended primarily for MSc students. Prerequisite: STAT 471 or consent of Department.
STAT 578 - Regression Analysis
Multiple linear regression, ordinary and generalized least squares, partial and multiple correlation. Regression diagnostics, collinearity, model building. Nonlinear regression. Selected topics: robust and nonparametric regression, measurement error models. Prerequisites: STAT 378 and a 400-level statistics course.