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Generalized linear and nonlinear models for correlated data : theory and applications using SAS /
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 mo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cary, NC :
SAS Institute,
Ã2012.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Vonesh, Edward F., |d 1952- | |
| 245 | 1 | 0 | |a Generalized linear and nonlinear models for correlated data : |b theory and applications using SAS / |c Edward F. Vonesh. |
| 260 | |a Cary, NC : |b SAS Institute, |c Ã2012. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references (pages 515-526) and index. | ||
| 505 | 0 | |a Introduction -- Marginal linera models : normal theory -- Linear mixed-effects models : normal theory -- Generalized linear and nonlinear models -- Generalized linear and nonlinear mixed-effects models -- Missing data in longitudinal clinical trials -- Additional topics and applications. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a 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. | ||
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| 650 | 0 | |a Linear models (Statistics) | |
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| 650 | 7 | |a Linear models (Statistics) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Vonesh, Edward F. |t Generalized linear and nonlinear models for correlated data. |d Cary, NC : SAS Institute, 2012 |z 9781599946474 |w (OCoLC)808418295 |
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