Welcome to Central Library, SUST
Amazon cover image
Image from Amazon.com
Image from Google Jackets

Introduction to multivariate analysis : linear and nonlinear modeling / Sadanori Konishi.

By: Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science seriesPublisher: Boca Raton : Chapman & Hall/CRC, 2014Edition: 1stDescription: xxv, 312 p. : ill. ; 22 cmContent type:
  • text
  • still image
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781482256222 (ePub ebook) :
  • 9781466567290 (PDF ebook) :
  • 9781482256215 (VitalBook ebook) :
Subject(s): Additional physical formats: Print version:: No titleDDC classification:
  • 519.535 23 KOI
Contents:
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
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)

Academic

Includes bibliographical references and index.

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

Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK). UkOxU

Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. UkOxU

Description based on CIP data; item not viewed.

There are no comments on this title.

to post a comment.