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[Sadanori Konishi] Introduction to Multivariate Analysis : Linear and Nonlinear Modeling(pdf){Zzzzz} [BЯ]

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  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.
  
  Editorial Reviews
 
Review
  
  "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
  
  Publisher: Chapman and Hall/CRC; 1 edition (June 6, 2014)
  Language: English
  ISBN-10:
1466567287
  ISBN-13: 978-1466567283
  
   						

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