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Introduction to Multivariate Analysis - Linear and Nonlinear Modeling (Paperback)
Loot Price: R1,521
Discovery Miles 15 210
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Introduction to Multivariate Analysis - Linear and Nonlinear Modeling (Paperback)
Series: Chapman & Hall/CRC Texts in Statistical Science
Expected to ship within 12 - 17 working days
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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.
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