Introduction to Multivariate Analysis

This text shows how to use multivariate analysis to extract useful information from multivariate data and understand the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It prima

Introduction. Linear Regression Models. Nonlinear Regression Models. Logistic Regression Models. Model Evaluation and Selection. Discriminant Analysis. Bayesian Classification. Support Vector Machines. Principal Component Analysis. Clustering. Appendices. Bibliography. Index.