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 23 articles )

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  1. Li, Liang; Wu, Chih-Hsien; Ning, Jing; Huang, Xuelin; Shih, Ya-Chen Tina; Shen, Yu: Semiparametric estimation of longitudinal medical cost trajectory (2018)
  2. Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
  3. Huang, Shujuan; Hartman, Brian; Brazauskas, Vytaras: Model selection and averaging of health costs in episode treatment groups (2017)
  4. Christensen, Ronald: Analysis of variance, design and regression. Linear modeling for unbalanced data (2016)
  5. 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)
  6. Luo, Ji; Zhang, Jiajia; Sun, Han: Estimation of relative risk using a log-binomial model with constraints (2014)
  7. Zhou, Rong; Sivaganesan, Siva; Longla, Martial: An objective Bayesian estimation of parameters in a log-binomial model (2014)
  8. Petersen, Martin R.; Deddens, James A.: Maximum likelihood estimation of the log-binomial model (2010)
  9. Yang, Zhao; Hardin, James W.; Addy, Cheryl L.: Score tests for overdispersion in zero-inflated Poisson mixed models (2010)
  10. Klein JP Gerster M, Andersen PK, Tarima S, Perme MP: SAS and R functions to compute pseudo-values for censored data regression (2008) not zbMATH
  11. Brown, Helen; Prescott, Robin: Applied mixed models in medicine. (2006)
  12. Preisser, John S.; Garcia, Daniel I.: Alternative computational formulae for generalized linear model diagnostics: Identifying influential observations with SAS software (2005)
  13. Lawal, H. Bayo: Using a GLM to decompose the symmetry model in square contingency tables with ordered categories (2004)
  14. Roy, Jason; Lin, Xihong; Ryan, Louise M.: Scaled marginal models for multiple continuous outcomes (2003)
  15. H. Bayo Lawal; Richard Sundheim: Generating Factor Variables for Asymmetry, Non-independence and Skew-symmetry Models in Square Contingency Tables using SAS (2002) not zbMATH
  16. Lawal, H. Bayo: Modeling the 1984-1993 American League baseball results as dependent categorical data (2002)
  17. Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban: Interval estimation for a binomial proportion. (With comments and a rejoinder). (2001)
  18. Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven: Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations. (2001)
  19. Petersen, Martin R.; Deddens, James A.: Effects of omitting a covariate in Poisson models when the data are balanced (2000)
  20. Tu, Xin M.; Jia, Gang; Kowalski, Jeanne; Bacanu, Silviu A.: Bayesian regression analysis of data with censored initiating and terminating times: Applications to AIDS (2000)

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