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D�tails bibliographiques

Titre: Introduction to Multivariate Analysis: Linear and Nonlinear Modeling (Chapman & Hall/CRC Texts in Statistical Science)
�diteur: Chapman and Hall/CRC
Date de parution: 2014
Langue: anglais
ISBN � 10 chiffres: 1466567287
ISBN � 13 chiffres: 9781466567283
Reliure: Couverture rigide
�tat de l'article: Good

A propos de ce titre

Synopsis

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.

� propos de l?auteur

Sadanori Konishi

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