Ebook: Introduction to multivariate analysis: linear and nonlinear modeling

""The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets.""--Fabio Rapallo, Zentralblatt MATH 1296.;Front Cover; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. Linear Regression Models; 3. Nonlinear Regression Models; 4. Logistic Regression Models; 5. Model Evaluation and Selection; 6. Discriminant Analysis; 7. Bayesian Classification; 8. Support Vector Machines; 9. Principal Component Analysis; 10. Clustering; A. Bootstrap Methods; B. Lagrange Multipliers; C. EM Algorithm; Bibliography.