Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear models, ... machine learning and big data study, with R - Softcover

Marden, John I

 
9781456538835: Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear models, ... machine learning and big data study, with R
Softcover
ISBN 10: 1456538837 ISBN 13: 9781456538835
Verlag: CreateSpace Independent Publishing Platform, 2015

Inhaltsangabe

Multivariate Statistics: Old School is a mathematical and methodological introduction to multivariate statistical analysis. It presents the basic mathematical grounding that graduate statistics students need for future research, and important multivariate techniques useful to statisticians in general. The material provides support for further study in big data and machine learning.

Topics include

  • The multivariate normal and Wishart distributions
  • Linear models, including multivariate regression and analysis of variance, and both-sides models (GMANOVA, repeated measures, growth curves)
  • Linear algebra useful for multivariate statistics
  • Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models
  • Classification (linear and quadratic discrimination, trees, logistic regression)
  • Clustering (K-means, model-based, hierarchical)
  • Other techniques, including biplots, canonical correlations, and multidimensional scaling
Most of the analyses in the book use the statistical computing environment R, for which there is an available package (msos) of multivariate routines and data sets.

This text was developed over many years by the author, John Marden, while teaching in the Department of Statistics, University of Illinois at Urbana-Champaign.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Reseña del editor

Multivariate Statistics: Old School is a mathematical and methodological introduction to multivariate statistical analysis. It presents the basic mathematical grounding that graduate statistics students need for future research, and important multivariate techniques useful to statisticians in general. The material provides support for further study in big data and machine learning.

Topics include

  • The multivariate normal and Wishart distributions
  • Linear models, including multivariate regression and analysis of variance, and both-sides models (GMANOVA, repeated measures, growth curves)
  • Linear algebra useful for multivariate statistics
  • Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models
  • Classification (linear and quadratic discrimination, trees, logistic regression)
  • Clustering (K-means, model-based, hierarchical)
  • Other techniques, including biplots, canonical correlations, and multidimensional scaling
Most of the analyses in the book use the statistical computing environment R, for which there is an available package (msos) of multivariate routines and data sets.

This text was developed over many years by the author, John Marden, while teaching in the Department of Statistics, University of Illinois at Urbana-Champaign.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
CreateSpace Independent Publishing Platform
Erscheinungsdatum
2015
Sprache
Englisch
ISBN 10 
1456538837
ISBN 13 
9781456538835
Einband
Tapa blanda
Anzahl der Seiten
358
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar