SAS for mixed models /

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The subject of mixed linear models is taught in graduate-level statistics courses and is familiar to most statisticians. During the past 10 years, use of mixed model methodology has expanded to nearly all areas of statistical applications. It is routinely taught and applied even in disciplines outsi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Littell, Ramon C., Milliken, George A., 1943- (Autor), Stroup, Walter W. (Walter Whitney) (Autor), Wolfinger, Russell Dean (Autor), Schabenberger, Oliver (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, NC : SAS Institute, 2007.
Edición:Second edition.
Temas:
SAS (Computer file)
Mathematical statistics > Data processing.
Electronic data processing.
Computer science.
Statistics.
Electronic Data Processing
Statistique mathématique > Informatique.
Informatique.
Instructional and educational works.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Introduction
  • Randomized block designs
  • Random effects models
  • Multi-factor treatment designs with multiple error terms
  • Analysis of repeated measures data
  • Best linear unbiased prediction
  • Analysis of covariance
  • Random coefficient models
  • Heterogeneous variance models
  • Mixed model diagnostics
  • Spatial variability
  • Power calculations for mixed models
  • Some Bayesian approaches to mixed models
  • Generalized linear mixed models
  • Nonlinear mixed models
  • Case studies
  • Appendix 1. Linear mixed model theory
  • Appendix 2. Data sets.

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