Separation-resistant and bias-reduced logistic regression: STATISTICA macro

Journal of Statistical Software

Volumn 47, Issue , 2012, Pages

  Fijorek, Kamil ^a   Sokolowski, Andrzej ^a  

^a CRACOW UNIVERSITY OF ECONOMICS   (Poland)

Author keywords

Complete separation; Logistic regression; STATISTICA

Indexed keywords

EID: 84863307437     PISSN: None     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v047.c02     Document Type: Article

References (20)

1. Albert A, Anderson JA (1984). On the Existence of Maximum Likelihood Estimates in Logistic Regression Models. Biometrika, 71(1), 1-10.
2. Bull SB, Mak C, Greenwood CMT (2002). A Modified Score Function Estimator for Multinomial Logistic Regression in Small Samples. Computational Statistics & Data Analysis, 39, 57-74.
3. Chen MH, Ibrahim JG, Kim S (2008). Properties and Implementation of Jeffreys's Prior in Binomial Regression Models. Journal of the American Statistical Association, 103(484), 1659-1664.
4. Christmann A, Rousseeuw PJ (2001). Measuring Overlap in Logistic Regression. Computational Statistics & Data Analysis, 37, 65-75.
5. Firth D (1992a). Bias Reduction, the Jeffreys Prior and GLIM. In R Gilchrist, G Tutz (eds.), Advances in GLIM and Statistical Modelling, pp. 91-100. Springer-Verlag.
6. Firth D (1992b). Generalized Linear Models and Jeffreys Priors: An Iterative Weighted Leastsquares Approach. In Y Dodge, J Whittaker (eds.), Computational Statistics, volume 1, pp. 553-557. Physica-Verlag, Heidelberg.
7. Firth D (1993). Bias Reduction of Maximum Likelihood Estimates. Biometrika, 80(1), 27-38.
8. Flury B (1988). Multivariate Statistics: A Practical Approach. Chapman & Hall, London.
9. Greene WH (2003). Econometric Analysis. 5th edition. Prentice Hall, Upper Saddle River.
10. Heinze G (1999). The Application of Firth's Procedure to Cox and Logistic Regression. Technical Report 10, Section of Clinical Biometrics, Department of Medical Computer Sciences, University of Vienna, Vienna, Austria.
11. Heinze G (2006). A Comparative Investigation of Methods for Logistic Regression with Separated or Nearly Separated Data. Statistics in Medicine, 25(24), 4216-4226.
12. Heinze G, Ploner M (2004). A SAS Macro, S-PLUS Library and R Package to Perform Logistic Regression without Convergence Problems. Technical report, Section of Clinical Biometrics, Department of Medical Computer Sciences, Medical University of Vienna, Vienna, Austria.
13. Heinze G, Schemper M (2002). A Solution to the Problem of Separation in Logistic Regression. Statistics in Medicine, 21(16), 2409-2419.
14. Hosmer DW, Lemeshow S (2000). Applied Logistic Regression. John Wiley & Sons.
15. Kosmidis I (2011). brglm: Bias Reduction in Binary-Response GLMs. R package version 0.5-6, URL http://CRAN.R-project.org/package=brglm.
16. Kosmidis I, Firth D (2009). Bias Reduction in Exponential Family Nonlinear Models. Biometrika, 96(4), 793-804.
17. Long JS (1997). Regression Models for Categorical and Limited Dependent Variables. Sage Publications, Thousand Oaks.
18. Ploner M, Dunkler D, Southworth H, Heinze G (2010). logistf: Firth's Bias Reduced Logistic Regression. R package version 1.10, URL http://CRAN.R-project.org/packagelogistf.
19. R Development Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
20. StatSoft, Inc (2010). STATISTICA Version 9.1. URL http://www.statsoft.com/.