The binary regression quantile plot: Assessing the importance of predictors in binary regression visually

Biometrical Journal

Volumn 43, Issue 1, 2001, Pages 5-21

  Bura, Efstathia ^a   Gastwirth, Joseph L ^b  

^a GEORGE WASHINGTON UNIVERSITY   (United States)

^b NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Binary response; Explanatory power; Logistic regression; Quantile plot

Indexed keywords

BINARY REGRESSION; BINARY RESPONSE; DATA SET; EXPLANATORY POWER; LIKELIHOOD RATIO TESTS; LOGISTICS REGRESSIONS; QUANTILE PLOTS; REGRESSION MODELLING; REGRESSION QUANTILE; VISUAL COMPARISON;

REGRESSION ANALYSIS;

EID: 0035582323     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/1521-4036(200102)43:1<5::AID-BIMJ5>3.0.CO;2-6     Document Type: Article

References (27)

1. AGRESTI, A., 1990: Categorical Data Analysis. Wiley, New York.
2. AGRESTI, A., 1996: An Introduction to Categorical Data Analysis. Wiley, New York.
3. BAHADUR, R. R., 1966: A Note on Quantiles in Large Samples. The Annals of Mathematical Statistics 37, 577-580.
4. BILLINGSLEY, P., 1986: Probability and Measure. Wiley, New York.
5. CHRISTENSEN, R., 1997: Log-linear Models and Logistic Regression. Springer, New York.
6. COLLETT, D., 1991: Modelling Binary Data. Chapman & Hall, London.
7. COPAS, J., 1999: The effectiveness of risk scores: the logit rank plot. Applied Statistics 48, 165-183.
8. CSÖRGO, M., GASTWIRTH, J. L. and ZITIKIS, R. (to appear). Statistical Foundations for the Analysis of Expectancy Curves. Technical Report, Carleton University.
9. FELLER, W., 1966: An Introduction to Probability Theory and its Applications. Vol. 2. Wiley, New York.
10. FINNEY, D. J., 1971: Probit Analysis. 3rd edn. Cambridge University Press, Cambridge.
11. GASTWIRTH, J. L., 1971: On the Sign Test for Symmetry. Journal of the American Statistical Association 66, 821-823.
12. GHOSH, J. K., 1971: A New Proof of the Bahadur Representation of Quantiles and an Application. The Annals of Mathematical Statistics 42, 1957-1961.
13. GOODMAN, L. A. and KRUSKAL, W. H., 1979: Measures of Association for Cross Classifications. Springer-Verlag, New York.
14. HASTIE, T. J., and TIBSHIRANI, R. J., 1990: Generalized Additive Models. Chapman & Hall, London.
15. HOOK, E. B., 1981: Rates of Chromosomal Abnormalities at Different Maternal Ages. Obstetrics and Gynecology 58, 282-285.
16. KORN, E. L. and SIMON, R., 1991: Explained Residual Variation, Explained Risk, and Goodness of Fit. The American Statistician 45, 201-206.
17. HOSMER, P. W. and LEMESHOW, S., 1989: Applied Logistic Regression. Chapman & Hall, New York.
18. MCCULLAGH, P. and NELDER, J. A., 1989: Generalized Linear Models. Chapman & Hall, New York.
19. MILICER, H. and SZCZOTKA, F., 1966: Age at menarche in Warsaw girls in 1965. Human Biology 38, 199-203.
20. MITTLBÖCK, M. and SCHEMPER, M., 1996: Explained Variation for Logistic Regression. Statistics in Medicine 15, 1987-1997.
21. MORGAN, B. J. T., 1992: Analysis of Quantal Response Data. Chapman & Hall, London.
22. PIGEON, J. G. and HEYSE, J. F., 1999: An improved goodness of fit statistic for probability prediction models. Biometrical Journal 41, 71-82.
23. RAO, C. R. and ZHAO, L. C., 1995: Convergence theorems for empirical cumulative quantile regression function. Mathematical Methods of Statistics 4, 81-91.
24. RAO, C. R. and ZHAO, L. C., 1996: Law of the iterated logarithm for empirical cumulative quantile regression functions. Statistica Sinica 6, 693-702.
25. REYNOLDS, H. T., 1977: The Analysis of Cross-Classifications. The Free Press, New York.
26. SPRENT, P., 1998: Data Driven Statistical Methods. Chapman & Hall, London.
27. TIKU, M. L. and VAUGHAN, D. C., 1997: Logistic and Nonlogistic Density Functions in Binary Regression with Nonstochastic Covariates. Statistica Sinica 6, 693-702.