Book DetailAuthor/Editor(s): Sadanori Konishi
Publication Date: June 6, 2014
Publisher: Chapman and Hall/CRC
Size: 3.26 MB
Book DescriptionIntroduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding 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 primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering.
The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection.
- Explains how to use linear and nonlinear multivariate techniques to extract information from data and understand random phenomena
- Includes a self-contained introduction to theoretical results
- Presents many examples and figures that facilitate a deep understanding of multivariate analysis techniques
- Covers regression, discriminant analysis, Bayesian classification, support vector machines, principal component analysis, and clustering
- Incorporates real data sets from engineering, pattern recognition, medicine, and more
For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.
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