Multivariate data analysis

Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this books shows that non-parametric regression can be successfully applied to system identification, and it highlights the achievements in doing so. With emphasis on Hammerstein, Wiener systems, and their multidimensional extensions, the authors show how to identify nonlinear subsystems and their characteristics when limited information exists. Algorithms using trigonometric, Legendre, Laguerre, and Hermite series are investigated, and the kernel algorithm, its semirecursive versions, and fully recursive modifications are covered. The theories of modern non-parametric regression, approximation, and orthogonal expansions, along with new approaches to system identification (including semiparametric identification), are provided. Detailed information about all tools used is provided in the appendices. This book is for researchers and practitioners in systems theory, signal processing, and communications and will appeal to researchers in fields like mechanics, economics, and biology, where experimental data are used to obtain models of systems "Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis for the non-statistician. The seventh edition incorporates pivotal advances in technology that will assist students in gaining a firm understanding of statistical and managerial principles so as to develop a "comfort zone" not only for the statistical, but also the practical issues involved."--BOOK JACKET. Overview of multivariate methods -- pt. I. Preparing to apply multivariate analysis. Examining your data ; Exploratory factor analysis -- pt. II. Dependence techniques. Multiple regression analysis ; Multiple discriminant analysis ; Logistic regression : regression with a binary dependent variable ; MANOVA and GLM ; Conjoint analysis -- pt. III. Interdependent techniques. Cluster analysis ; Multidimensional scaling ; Analyzing nominal data with correspondence analysis -- pt. IV. Moving beyond the basic techniques. Structural equations modeling overview ; Confirmatory factor analysis ; Testing structural equations models ; Advanced SEM topics and PLS