Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models Texts in Statistical Science

Faraway, Julian James:

ISBN 10: 158488424X / ISBN 13: 9781584884248

Edité par CRC Press, 2006

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301 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 590.

N° de réf. du vendeur 2244218

Détails bibliographiques

• Titre: Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models Texts in Statistical Science
• Éditeur: CRC Press
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 158488424X
• ISBN à 13 chiffres: 9781584884248
• Reliure: gebundene Ausgabe
• État de l'article: Gut
• Catalogues du vendeur:
  • 90 Varia

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Synopsis

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies.

Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/

Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.