The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of maximum likelihood. It can also perform conditional logistic regression for binary response data and exact logistic regression for binary and nominal response data. The maximum likelihood estimation is carried out with either the Fisher scoring algorithm or the Newton-Raphson algorithm, and you can perform the bias-reducing penalized likelihood optimization as discussed by Firth (1993) and Heinze and Schemper (2002). You can specify starting values for the parameter estimates. The logit link function in the logistic regression models can be replaced by the probit function, the complementary log-log function, or the generalized logit function.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Huang, Shujuan; Hartman, Brian; Brazauskas, Vytaras: Model selection and averaging of health costs in episode treatment groups (2017)
- Christensen, Ronald: Analysis of variance, design and regression. Linear modeling for unbalanced data (2016)
- Jiaqiao Hu; Wei Zhu; Yi Su; Weng Kee Wong: Controlled Optimal Design Program for the Logit Dose Response Model (2010) not zbMATH
- Garnett McMillan; Timothy Hanson: SAS Macro BDM for Fitting the Dale Regression Model to Bivariate Ordinal Response Data (2005) not zbMATH
- Preisser, John S.; Garcia, Daniel I.: Alternative computational formulae for generalized linear model diagnostics: Identifying influential observations with SAS software (2005)
- Bull, Shelley B.; Mak, Carmen; Greenwood, Celia M. T.: A modified score function estimator for multinomial logistic regression in small samples. (2002)
- Lawal, H. Bayo: Modeling the 1984-1993 American League baseball results as dependent categorical data (2002)