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 "L"1 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.
General
Imprint: |
Crc Press
|
Country of origin: |
United States |
Series: |
Chapman & Hall/CRC Texts in Statistical Science |
Release date: |
June 2014 |
First published: |
2014 |
Authors: |
Sadanori Konishi
|
Dimensions: |
229 x 152 x 24mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
338 |
ISBN-13: |
978-1-4665-6728-3 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-4665-6728-7 |
Barcode: |
9781466567283 |
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