730—Multivariate Analysis. (3) (Prereq: STAT 713) A survey of the theory and applications of the fundamental techniques for analyzing multivariate data.
Usually Offered: Fall semesters, Even years
Purpose: To provide graduate students who have training in mathematical statistics with a solid introduction to the theory and implementation of the fundamental methods of multivariate statistics, and to give an overview of some recent developments. Major topics include procedures such as principal component and factor analysis, discriminant analysis, multivariate analysis of variance, cluster analysis, and multidimensional scaling.
Mardia, K.V., Kent, J.T., and Bibby, J.M. (1980). Multivariate Analysis. New York: Academic Press.
Selected Other Useful Books:
Everitt, B. (2005). An R and S-PLUS Companion to Multivariate Analysis. London: Springer.
Stevens, J.P. (2009). Applied Multivariate Statistics for the Social Sciences. Fifth Edition. New York: Routledge.
Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S. Fourth Edition.. New York: Springer.
|Foundations of Multivariate Analysis: Properties of Random Vectors, Theory of The Multivariate Normal Distribution, Multivariate Regression Analysis||1-6||6 weeks|
|Principal Components and Exploratory Factor Analysis, Introduction to Confirmatory Factor Analysis and Structural Equation Modeling||8-9||2.3 weeks|
|Canonical Correlation Analysis||10||0.3 weeks|
|Discriminant Analysis and Multivariate Analysis of Variance||11-12||2.3 weeks|
|Classification and Regression Trees and Random Decision Forests||-||1 week|
|Hierarchical Clustering, Model Based Clustering, and Multidimensional Scaling||13-14||1.3 weeks|
|Hierarchical Linear Models (a.k.a. Multi-level Models or Random Coefficients Regression)||-||0.7 weeks|
Contact Faculty: Brian Habing