|Course:||STAT 24620=STAT 32950|
|Title:||Multivariate Statistical Analysis: Applications and Techniques|
|Teaching Assistant(s):||Jiacheng Wang and Ran Dai|
|Class Schedule:||Sec 01: TR 9:30 AM-10:50 AM in Stuart 101|
No required textbooks. A partial list of reference books:
|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.
Prerequisites: STAT 24400-24500 or STAT 24410-24510 or consent of instructor