Model selection in logistic regression and performance of its predictive ability

Australian Journal of Basic and Applied Sciences

Volumn 4, Issue 12, 2010, Pages 5813-5822

  Sarkar, S K ^a   Midi, Habshah ^a   Rana, Sohel ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Best subsets; Binary response; C index; Information criteria; Likelihood ratio test; Somers' D

Indexed keywords

EID: 79951565274     PISSN: 19918178     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. Akaike, H., 1974. A new look at a statistical model identification. IEEE Transactions in Automatic Control, 19: 716-723.
2. Copas, J.B., 1983. Regression Prediction and Shrinkage. Journal of Royal Statistical Society, B45: 311-354.
3. Cox, D.R. and E.J. Snell, 1989. The Analysis of Binary Data, 2nd edition. Chapman and Hall, London.
4. Hanley, J.A. and B.J. McNeil, 1982. The measure and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143: 29-36.
5. Harrel, F.E., 2001. Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer-Verlag, New York, Inc.
6. Hauck, W.W. and A. Donner, 1977. Wald's Test as applied to hypotheses in logit analysis. Journal of the American Statistical Association, 72: 851-853.
7. Hosmer, D.W. and S. Lemeshow, 2000. Applied Logistic Regression, 2nd edition. Wiley, New York.
8. Hosmer, D.W., B. Jovanovic and S. Lemeshow, 1989. Best Subsets Logistic Regression. Biometrics, 45:1265-1270.
9. Hosmer, T., D.W. Hosmer and L.L. Fisher, 1983. A comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrete variables. Communications in Statistics, B12: 577-593.
10. Jennings, D.E., 1986. Judging inference adequacy in logistic regression. Journal of the American Statistical Association, 81: 471-476.
11. Kiezun, A., T.A. Lee and N. Shomron, 2009. Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infraction. Bioinformation, 3(7): 311-313.
12. Lawless, J.F. and K. Singhal, 1987. ISMOD: An all-subsets regression program for generalized linear models, I. Statistical and Computational background. Computer methods and Programs in Biomedicine, 24: 117-124.
13. Lawless, J.F. and K. Singhal, 1978. Efficient screening of nonnormal regression models. Biometrics, 34: 318-327. p
14. Mallows, C.L., 1973. Some comments on C. Technometrics, 15: 661-676.
15. Moulton, L.H., L.A. Weissfeld and R.T. St.Laurent, 1993. Bartlett correction factors in logistic regression models. Computational Statistics and Data Analysis, 15: 01-11.
16. Nagelkerke, N.J.D., 1991. A note on the general definition of the coefficient of determination. Biometrika, 78: 691-692.
17. Pregibon, D., 1981. Logistic regression diagnostics. Annals of Statistics, 9: 705-724.
18. Rencher, A.C. and F.C. Pun, 1980. Inflation of R in Best Subset Regression. Technometrics, 2 22: 49-54.
19. Ryan, T.P., 1997. Modern Regression Methods. John Wiley & Sons, Inc.
20. Sarkar, S.K. and H. Midi, 2009. Optimization Techniques for Variable Selection in Binary Logistic Regression Model Applied to Desire for Children Data. Journal of Mathematics and Statistics, 5(4): 387-394.
21. Schuster, C., 1998. Regression Analysis for Social Sciences. Academic Press, New York.
22. Shtatland, E.S., E. Cain and M.B. Barton, 2001. The Perils of Stepwise Logistic Regression and how to escape them using Information Criteria and the Output Delivery System, SUGI'26, Proceeding, Paper, 222-26, Cary, NC: SAS Institute, Inc.
23. Stone, M., 1977. An asymptotic equivalence of choice of model by cross-validation and Akaike's criterion. Journal of the Royal Statistical Society, B39: 44-47.