Variable selection for logistic regression using a prediction-focused information criterion

Biometrics

Volumn 62, Issue 4, 2006, Pages 972-979

  Claeskens, Gerda ^a   Croux, Christophe ^a   Van Kerckhoven, Johan ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

Error rate; Focused information criterion; Forward selection; Logistic regression; Model selection; Risk measures

Indexed keywords

ERRORS; EYE PROTECTION; INFORMATION USE; MEAN SQUARE ERROR; REGRESSION ANALYSIS; RISK ASSESSMENT;

ERROR RATE; FOCUSED INFORMATION CRITERIA; FORWARD SELECTION; INFORMATION CRITERION; LOGISTICS REGRESSIONS; MODEL SELECTION; QUANTITY OF INTEREST; RISK MEASURES; STANDARD VERSIONS; VARIABLES SELECTIONS;

MEDICAL APPLICATIONS;

BIOMETRY; ERROR ANALYSIS; NUMERICAL MODEL; PREDICTION; REGRESSION ANALYSIS;

ARTICLE; BIOSTATISTICS; DIABETIC RETINOPATHY; HUMAN; INFORMATION PROCESSING; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL MODEL; PREDICTION; RISK ASSESSMENT; SIMULATION;

EID: 33845481812     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00567.x     Document Type: Article

References (13)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-723.
2. Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edition. New York: Springer.
3. Claeskens, G. and Hjort, N. L. (2003). The focused information criterion (with discussion). Journal of the American Statistical Association 98, 900-916.
4. Davis, C. E., Hyde, J. E., Bangdiwala, S. I., and Nelson, J. J. (1986). An example of dependencies among variables in a conditional logistic regression. In Modern Statistical Methods in Chronic Disease Epidemiology, S. H. Moolgavkar and R. L. Prentice (eds). New York: Wiley.
5. George, E. I. (2000). The variable selection problem. Journal of the American Statistical Association 95, 1304-1308.
6. Hand, D. J. and Vinciotti, V. (2003). Local versus global models for classification problems: Fitting models where it matters. American Statistician 57, 124-131.
7. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
8. Hjort, N. L. and Claeskens, G. (2003). Frequentist model average estimators (with discussion). Journal of the American Statistical Association 98, 879-899.
9. Klein, R., Klein, B. E. K., Moss, S. E., Davis, M. D., and DeMets, D. L. (1984). The Wisconsin Epidemiologic Study of Diabetic Retinopathy: II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Archives of Ophthalmology 102, 520-526.
10. Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Hoboken, New Jersey: John Wiley and Sons.
11. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996). Applied Linear Statistical Models: Fourth Edition. Boston: McGraw-Hill.
12. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6, 461-464.
13. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-639.