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Generalized linear mixed models : modern concepts, methods and applications /
"Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press, Taylor & Francis Group,
[2013]
|
| Colección: | Texts in statistical science.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Stroup, Walter W. |q (Walter Whitney), |e author. | |
| 245 | 1 | 0 | |a Generalized linear mixed models : |b modern concepts, methods and applications / |c Walter W. Stroup. |
| 264 | 1 | |a Boca Raton : |b CRC Press, Taylor & Francis Group, |c [2013] | |
| 300 | |a 1 online resource (xxv, 529 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Texts in statistical science | |
| 504 | |a Includes bibliographical references (pages 513-518) and index. | ||
| 505 | 0 | |a Part I: The Big Picture -- 1. Modeling Basics -- 2. Design Matters -- 3. Setting the Stage -- Part II: Estimation and Inference Essentials -- 4. Estimation -- 5. Inference, Part I: Model Effects -- 6. Inference, Part II: Covariance Components -- Part III: Working with GLMMs -- 7. Treatment and Explanatory Variable Structure -- 8. Multilevel Models -- 9. Best Linear Unbiased Prediction -- 10. Rates and Proportions -- 11. Counts -- 12. Time-to-Event Data -- 13. Multinomial Data -- 14. Correlated Errors, Part I: Repeated Measures -- 15. Correlated Errors, Part II: Spatial Variability -- 16. Power, Sample Size, and Planning. | |
| 520 | |a "Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random. With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling"-- |c Provided by publisher | ||
| 588 | 0 | |a Print resource; title from PDF title page (viewed January 19, 2016). | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Linear models (Statistics) | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Linear models (Statistics) |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Stroup, Walter W. (Walter Whitney). |t Generalized linear mixed models. |d Boca Raton : CRC Press, Taylor & Francis Group, [2013] |z 9781439815120 |w (DLC) 2012025214 |w (OCoLC)742512037 |
| 830 | 0 | |a Texts in statistical science. | |
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