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. ..

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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. Janani, Leila; Mansournia, Mohammad Ali; Mohammad, Kazem; Mahmoodi, Mahmood; Mehrabani, Kamran; Nourijelyani, Keramat: Comparison between Bayesian approach and frequentist methods for estimating relative risk in randomized controlled trials: a simulation study (2017)
  5. Liu, Xueyan; Winter, Bryan; Tang, Li; Zhang, Bo; Zhang, Zhiwei; Zhang, Hui: Simulating comparisons of different computing algorithms fitting zero-inflated Poisson models for zero abundant counts (2017)
  6. Batsidis, A.; Economou, P.; Tzavelas, G.: Tests of fit for a lognormal distribution (2016)
  7. Christensen, Ronald: Analysis of variance, design and regression. Linear modeling for unbalanced data (2016)
  8. 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)
  9. Luo, Ji; Zhang, Jiajia; Sun, Han: Estimation of relative risk using a log-binomial model with constraints (2014)
  10. Zhou, Rong; Sivaganesan, Siva; Longla, Martial: An objective Bayesian estimation of parameters in a log-binomial model (2014)
  11. Hassan, M. Y.; El-Bassiouni, M. Y.: Modelling Poisson marked point processes using bivariate mixture transition distributions (2013)
  12. Petersen, Martin R.; Deddens, James A.: Maximum likelihood estimation of the log-binomial model (2010)
  13. Yang, Zhao; Hardin, James W.; Addy, Cheryl L.: Score tests for overdispersion in zero-inflated Poisson mixed models (2010)
  14. 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
  15. Brown, Helen; Prescott, Robin: Applied mixed models in medicine. (2006)
  16. Preisser, John S.; Garcia, Daniel I.: Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software (2005)
  17. Lawal, H. Bayo: Using a GLM to decompose the symmetry model in square contingency tables with ordered categories (2004)
  18. Roy, Jason; Lin, Xihong; Ryan, Louise M.: Scaled marginal models for multiple continuous outcomes (2003)
  19. 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
  20. Lawal, H. Bayo: Modeling the 1984-1993 American League baseball results as dependent categorical data (2002)

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