glimmix

The GLIMMIX procedure fits statistical models to data with correlations or nonconstant variability and where the response is not necessarily normally distributed. These models are known as generalized linear mixed models (GLMM). GLMMs, like linear mixed models, assume normal (Gaussian) random effects. Conditional on these random effects, data can have any distribution in the exponential family.


References in zbMATH (referenced in 12 articles )

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  1. Jones, Byron; Kenward, Michael G.: Design and analysis of cross-over trials (2015)
  2. Stroup, Walter W.: Generalized linear mixed models. Modern concepts, methods and applications. (2013)
  3. Wang, Peng; Tsai, Guei-Feng; Qu, Annie: Conditional inference functions for mixed-effects models with unspecified random-effects distribution (2012)
  4. Yang, Zhao; Hardin, James W.; Addy, Cheryl L.: Score tests for overdispersion in zero-inflated Poisson mixed models (2010)
  5. Meza, Cristian; Jaffrézic, Florence; Foulley, Jean-Louis: Estimation in the probit normal model for binary outcomes using the SAEM algorithm (2009)
  6. Schmid, Kendra; Marx, David; Samal, Ashok: Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios (2008)
  7. Yu, Q.; Tang, W.; Ma, Y.; Gamble, S.A.; Tu, X.M.: Comparing multiple sensitivities and specificities with different diagnostic criteria: applications to sexual abuse and sexual health research (2008)
  8. Dean, C. B.; Nielsen, Jason D.: Generalized linear mixed models: a review and some extensions (2007)
  9. Brown, Helen; Prescott, Robin: Applied mixed models in medicine. (2006)
  10. Zhao, Y.; Staudenmayer, J.; Coull, B.A.; Wand, M.P.: General design Bayesian generalized linear mixed models (2006)
  11. Molenberghs, Geert; Verbeke, Geert: Models for discrete longitudinal data. (2005)
  12. Rasmussen, Søren: Modelling of discrete spatial variation in epidemiology with SAS using GLIMMIX. (2004)