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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Konishi, Sadanori. Introduction to Multivariate Analysis : Linear and Nonlinear Modeling. Hoboken : CRC Press, ©2014 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Sadanori Konishi |

ISBN: | 9781466567290 1466567295 |

OCLC Number: | 908078757 |

Description: | 1 online resource (336 pages). |

Contents: | Front Cover; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. Linear Regression Models; 3. Nonlinear Regression Models; 4. Logistic Regression Models; 5. Model Evaluation and Selection; 6. Discriminant Analysis; 7. Bayesian Classification; 8. Support Vector Machines; 9. Principal Component Analysis; 10. Clustering; A. Bootstrap Methods; B. Lagrange Multipliers; C. EM Algorithm; Bibliography. |

Series Title: | Chapman & Hall/CRC Texts in Statistical Science. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

Select the Optimal Model for Interpreting Multivariate DataIntroduction 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. Introduction Regression Modeling Classification and Discrimination Dimension Reduction Clustering Linear Regression Models Relationship between Two Variables Relationships Involving Multiple Variables Regularization Nonlinear Regression Models Modeling Phenomena Modeling by Basis Functions Basis Expansions Regularization Logistic Regression Models Risk Prediction Models Multiple Risk Factor Models Nonlinear Logistic Regression Models Model Evaluation and SelectionCriteria Based on Prediction Errors Information Criteria Bayesian Model Evaluation Criterion Discriminant Analysis Fisher's Linear Discriminant Analysis Classification Based on Mahalanobis DistanceVariable Selection Canonical Discriminant Analysis Bayesian Classification Bayes' TheoremClassification with Gaussian Distributions Logistic Regression for Classification Support Vector Machines Separating Hyperplane Linearly Nonseparable CaseFrom Linear to Nonlinear Principal Component Analysis Principal Components Image Compression and Decompression Singular Value Decomposition Kernel Principal Component Analysis Clustering Hierarchical Clustering Nonhierarchical Clustering Mixture Models for Clustering Appendix A: Bootstrap Methods Appendix B: Lagrange Multipliers Appendix C: EM Algorithm Bibliography Index "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"This is an excellent textbook for upper-class undergraduate and graduate level students.ã The prerequisites are an introductory probability and statistics and linear algebra courses.ã To aid the student in the understanding and use of vector and matrix notations, and to emphasize that importance, the author appropriately uses the algebraic notation accompanied by the vector and matrix notations when needed; additionally, the accompanying geometrical interpretation are presented in clear diagrams.ã The writing style is crisp and clear.ã A pleasant format that the author used is to summarily review relevant topics in a narrative style to pave the way into a new topic.ã The textbook is accessible to students and researchers in the social sciences, econometrics, biomedical, computer and data science fields.ã This is the kind of textbook that a student or professional researcher will consult many times."-Stephen Hyatt, International Technological Universityã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã ã Introduction Regression Modeling Classification and Discrimination Dimension Reduction Clustering Linear Regression Models Relationship between Two Variables Relationships Involving Multiple Variables Regularization Nonlinear Regression Models Modeling Phenomena Modeling by Basis Functions Basis Expansions Regularization Logistic Regression Models Risk Prediction Models Multiple Risk Factor Models Nonlinear Logistic Regression Models Model Evaluation and Selection Criteria Based on Prediction Errors Information Criteria Bayesian Model Evaluation Criterion Discriminant Analysis Fisher's Linear Discriminant Analysis Classification Based on Mahalanobis Distance Variable Selection Canonical Discriminant Analysis Bayesian Classification Bayes' Theorem Classification with Gaussian Distributions Logistic Regression for Classification Support Vector Machines Separating Hyperplane Linearly Nonseparable Case From Linear to Nonlinear Principal Component Analysis Principal Components Image Compression and Decompression Singular Value Decomposition Kernel Principal Component Analysis Clustering Hierarchical Clustering Nonhierarchical Clustering Mixture Models for Clustering Appendix A: Bootstrap Methods Appendix B: Lagrange Multipliers Appendix C: EM Algorithm Bibliography Index 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. Read more...

*User-contributed reviews*