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 34 articles )

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  1. Nikoloulopoulos, Aristidis K.: Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response (2020)
  2. 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
  3. da Silva, José L. P.; Colosimo, Enrico A.; Demarqui, Fábio N.: A general GEE framework for the analysis of longitudinal ordinal missing data and related issues (2019)
  4. de Andrade, Bernardo Borba; Andrade, Joanlise Marco de Leon: Some results for maximum likelihood estimation of adjusted relative risks (2018)
  5. Li, Liang; Wu, Chih-Hsien; Ning, Jing; Huang, Xuelin; Shih, Ya-Chen Tina; Shen, Yu: Semiparametric estimation of longitudinal medical cost trajectory (2018)
  6. Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
  7. Huang, Shujuan; Hartman, Brian; Brazauskas, Vytaras: Model selection and averaging of health costs in episode treatment groups (2017)
  8. Inan, G.; Yucel, R.: Joint GEEs for multivariate correlated data with incomplete binary outcomes (2017)
  9. 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)
  10. 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)
  11. Batsidis, A.; Economou, P.; Tzavelas, G.: Tests of fit for a lognormal distribution (2016)
  12. Christensen, Ronald: Analysis of variance, design and regression. Linear modeling for unbalanced data (2016)
  13. 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)
  14. Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Lee, Soyoung; Tarwater, Patrick: Methods for estimating relative risk in studies of common binary outcomes (2014)
  15. Luo, Ji; Zhang, Jiajia; Sun, Han: Estimation of relative risk using a log-binomial model with constraints (2014)
  16. Nooraee, Nazanin; Molenberghs, Geert; van den Heuvel, Edwin R.: GEE for longitudinal ordinal data: comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN (2014)
  17. Zhou, Rong; Sivaganesan, Siva; Longla, Martial: An objective Bayesian estimation of parameters in a log-binomial model (2014)
  18. Hassan, M. Y.; El-Bassiouni, M. Y.: Modelling Poisson marked point processes using bivariate mixture transition distributions (2013)
  19. Petersen, Martin R.; Deddens, James A.: Maximum likelihood estimation of the log-binomial model (2010)
  20. Yang, Zhao; Hardin, James W.; Addy, Cheryl L.: Score tests for overdispersion in zero-inflated Poisson mixed models (2010)

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