Introduction to multivariate analysis : linear and nonlinear modeling
著者
書誌事項
Introduction to multivariate analysis : linear and nonlinear modeling
（Texts in statistical science）
CRC Press, Taylor & Francis Group, c2014
- タイトル別名
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Tahenryo keiseki nyumon : senkei kara hisenkei e
Tahenryo kaiseki nyumon : senkei kara hisenkei e
多変量解析入門 : 線形から非線形へ
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注記
Originally published in Japanese by Iwanami Shoten in 2010 under title: Tahenryo keiseki [sic] nyumon: senkei kara hisenkei e
"A Chapman & Hall book"
Bibliography: p. 299-307
Includes index
内容説明・目次
内容説明
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.
目次
Introduction. Linear Regression Models. Nonlinear Regression Models. Logistic Regression Models. Model Evaluation and Selection. Discriminant Analysis. Bayesian Classification. Support Vector Machines. Principal Component Analysis. Clustering. Appendices. Bibliography. Index.
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