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Course: STAT 24620=STAT 32950
Title: Multivariate Statistical Analysis: Applications and Techniques
Instructor(s): Mei Wang
Teaching Assistant(s): Byol Kim
Class Schedule: Sec 01: TR 9:30-10:50 AM in Stuart 101
Office Hours:  
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)

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