Course: STAT 24620=STAT 32950
Title: Multivariate Statistical Analysis: Applications and Techniques
Instructor(s): Mei Wang
Class Schedule: Sec 1: TR 9:30 AM–10:50 AM in Eckhart 133
Textbook(s): Johnson, Wichern, Applied Multivariate Statistical Analysis (6th edition)
Description: This course focuses on applications and techniques for analysis of multivariate and high dimensional data. Beginning subjects cover common multivariate techniques and dimension reduction, including principal component analysis, factor model, canonical correlation, multi-dimensional scaling, discriminant analysis, clustering, and correspondence analysis (if time permits). Further topics on statistical learning for high dimensional data and complex structures include penalized regression models (LASSO, ridge, elastic net), sparse PCA, independent component analysis, Gaussian mixture model, Expectation-Maximization methods, and random forest. Theoretical derivations will be presented with emphasis on motivations, applications, and hands-on data analysis.