SAS PROC 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 29 articles )

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  1. Tracie L. Shing, John S. Preisser, Richard C. Zink: GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations (2020) arXiv
  2. Oh, Minkyung; Mun, Jungwon: Diagnostics for repeated measurements in generalized linear mixed effects models (2019)
  3. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  4. Irimata, Kyle M.; Wilson, Jeffrey R.: Identifying intraclass correlations necessitating hierarchical modeling (2018)
  5. Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
  6. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  7. García-Escudero, L. A.; Gordaliza, A.; Greselin, F.; Ingrassia, S.; Mayo-Iscar, A.: Robust estimation of mixtures of regressions with random covariates, via trimming and constraints (2017)
  8. Tango, Toshiro: Repeated measures design with generalized linear mixed models for randomized controlled trials (2017)
  9. Jones, Byron; Kenward, Michael G.: Design and analysis of cross-over trials (2015)
  10. Liang Xie and Laurence Madden: %HPGLIMMIX: A High-Performance SAS Macro for GLMM Estimation (2014) not zbMATH
  11. Givens, G. H.; Beveridge, J. R.; Phillips, P. J.; Draper, B.; Lui, Y. M.; Bolme, D.: Introduction to face recognition and evaluation of algorithm performance (2013)
  12. Stroup, Walter W.: Generalized linear mixed models. Modern concepts, methods and applications. (2013)
  13. Bacci, Silvia: Longitudinal data: different approaches in the context of item-response theory models (2012)
  14. Wang, Peng; Tsai, Guei-Feng; Qu, Annie: Conditional inference functions for mixed-effects models with unspecified random-effects distribution (2012)
  15. Yang, Zhao; Hardin, James W.; Addy, Cheryl L.: Score tests for overdispersion in zero-inflated Poisson mixed models (2010)
  16. Meza, Cristian; Jaffrézic, Florence; Foulley, Jean-Louis: Estimation in the probit normal model for binary outcomes using the SAEM algorithm (2009)
  17. Schmid, Kendra; Marx, David; Samal, Ashok: Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios (2008) ioport
  18. 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)
  19. Dean, C. B.; Nielsen, Jason D.: Generalized linear mixed models: a review and some extensions (2007)
  20. Namata, Harriet; Shkedy, Ziv; Faes, Christel; Aerts, Marc; Molenberghs, Geert; Theeten, Heide; van Damme, Pierre; Beutels, Philippe: Estimation of the force of infection from current status data using generalized linear mixed models (2007)

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