Spring 2021 STAT 24620

Course: STAT 24620=STAT 32950

Title: Multivariate Statistical Analysis: Applications and Techniques

Instructor(s): Mei Wang

Teaching Assistant(s): Xialiang Dou, Qing Yan

Class Schedule: Section 1: TR 9:40 AM-11:00 AM (Remote)

Textbook(s): No required textbooks. A partial list of reference books:

Johnson and Wichern, Applied Multivariate Statistical Analysis (2007)
Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning (2009)
Bishop, Pattern Recognition and Machine Learning (2006)
Efron and Hastie, Computer Age Statistical Inference (2016)

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