Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS - Couverture souple

Vonesh, Edward F.

 
9781599946474: Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS
Couverture souple
ISBN 10 : 1599946475 ISBN 13 : 9781599946474
Editeur : SAS Institute, 2012

Synopsis

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

Biographie de l'auteur

Edward F. Vonesh, Ph.D., is self-employed as managing member of Vonesh Statistical Consulting, LLC, as well as a part-time employee of Northwestern University, where he supports research in his capacity as professor in the Department of Preventive Medicine. The author of numerous professional papers, he has published in the Journal of the American Statistical Association, Biometrika, Biometrics, and Statistics in Medicine. After twenty-nine years at Baxter Healthcare, Dr. Vonesh recently retired from his position as a senior Baxter research scientist. A SAS user since 1974, he received his B.S. and M.S. from Northern Illinois University and his Ph.D. in biostatistics from the University of Michigan and is a Fellow of the American Statistical Association.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
SAS Institute
Date d'édition
2012
Langue
anglais
ISBN 10 
1599946475
ISBN 13 
9781599946474
Reliure
Broché
Nombre de pages
552
Coordonnées du fabricant
non disponible
Personne responsable
gpsr@libri.de
gpsr@libri.de

Europaallee 1
Bad Hersfeld
36244
Allemagne

Autres éditions populaires du même titre

9781642953268: Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS

Edition présentée

ISBN 10 :  1642953261 ISBN 13 :  9781642953268
Editeur : SAS Institute, 2019
Couverture rigide