PROC GENMOD

The GENMOD procedure fits generalized linear models, as defined by Nelder and Wedderburn (1972). The class of generalized linear models is an extension of traditional linear models that allows the mean of a population to depend on a linear predictor through a nonlinear link function and allows the response probability distribution to be any member of an exponential family of distributions. Many widely used statistical models are generalized linear models. These include classical linear models with normal errors, logistic and probit models for binary data, and log-linear models for multinomial data. Many other useful statistical models can be formulated as generalized linear models by the selection of an appropriate link function and response probability distribution. ..


References in zbMATH (referenced in 13 articles )

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  1. Christensen, Ronald: Analysis of variance, design and regression. Linear modeling for unbalanced data (2016)
  2. Prague, Melanie; Wang, Rui; Stephens, Alisa; Tchetgen Tchetgen, Eric; Degruttola, Victor: Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes (2016)
  3. Luo, Ji; Zhang, Jiajia; Sun, Han: Estimation of relative risk using a log-binomial model with constraints (2014)
  4. Zhou, Rong; Sivaganesan, Siva; Longla, Martial: An objective Bayesian estimation of parameters in a log-binomial model (2014)
  5. Petersen, Martin R.; Deddens, James A.: Maximum likelihood estimation of the log-binomial model (2010)
  6. Brown, Helen; Prescott, Robin: Applied mixed models in medicine. (2006)
  7. Preisser, John S.; Garcia, Daniel I.: Alternative computational formulae for generalized linear model diagnostics: Identifying influential observations with SAS software (2005)
  8. Lawal, H.Bayo: Using a GLM to decompose the symmetry model in square contingency tables with ordered categories (2004)
  9. Roy, Jason; Lin, Xihong; Ryan, Louise M.: Scaled marginal models for multiple continuous outcomes (2003)
  10. Lawal, H.Bayo: Modeling the 1984-1993 American League baseball results as dependent categorical data (2002)
  11. Brown, Lawrence D.; Cai, T.Tony; DasGupta, Anirban: Interval estimation for a binomial proportion. (With comments and a rejoinder). (2001)
  12. Petersen, Martin R.; Deddens, James A.: Effects of omitting a covariate in Poisson models when the data are balanced (2000)
  13. Ziegler, Andreas; Grömping, Ulrike: The generalised estimating equations: A comparison of procedures available in commercial statistical software packages (1998)