Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science) por Sadanori Konishi

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science) por Sadanori Konishi
Titulo del libro : Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science)
Fecha de lanzamiento : June 6, 2014
Autor : Sadanori Konishi
Número de páginas : 338
Editor : Chapman and Hall/CRC

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Sadanori Konishi con Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science)

Select the Optimal Model for Interpreting Multivariate Data


Introduction 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.



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.