Probability estimation and machine learning-Editorial

Biometrical Journal

Volumn 56, Issue 4, 2014, Pages 531-533

  Schumacher, Martin ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

MACHINE-LEARNING; PROBABILITY ESTIMATION;

LEARNING SYSTEMS;

ARTIFICIAL INTELLIGENCE; PROBABILITY;

ARTIFICIAL INTELLIGENCE; PROBABILITY;

EID: 84903514071     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201400075     Document Type: Editorial

References (22)

1. Berk, R. A. (2010). Statistical Learning from a Regression Perspective. Springer, New York.
2. Binder, H. (2014). What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction? Biometrical Journal 56, 584-587.
3. Binder, H., Porzelius, C. and Schumacher, M. (2011). An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models. Biometrical Journal 53, 170-189.
4. Boulesteix, A.-L. and Schmid, M. (2014). Machine learning versus statistical modeling. Biometrical Journal 56, 588-593.
5. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review 78, 1-3.
6. Efron, B. and Tibshirani, R. (1997). Improvements on cross-validation: the .623+ bootstrap method. Journal of the American Statistical Association 92, 548-560.
7. Gerds, T. A., Cai, T. and Schumacher, M. (2008). The performance of risk prediction models. Biometrical Journal 50, 457-479.
8. Gerds, T. A. and Schumacher, M. (2007). Efron-type measures of prediction error for survival analysis. Biometrics 63, 1283-1287.
9. Habbema, J. D. and Hilden, J. (1981a). The measurement of performance in probabilistic diagnosis. IV. Utility considerations in therapeutics and prognostics. Methods of Information in Medicine 20, 80-96.
10. Habbema, J. D., Hilden, J. and Bjerregaard, B. (1978). The measurement of performance in probabilistic diagnosis. I. The problem, descriptive tools, and measures based on classification matrices. Methods of Information in Medicine 17, 217-226.
11. Habbema, J. D., Hilden, J. and Bjerregaard, B. (1981b). The measurement of performance in probabilistic diagnosis. V. General recommendations. Methods of Information in Medicine 20, 97-100.
12. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning; Data mining, Inference and Prediction (2nd edition). Springer, New York.
13. Hilden, J., Habbema, J. D. and Bjerregaard, B. (1978a). The measurement of performance in probabilistic diagnosis. II. Trustworthiness of the exact values of the diagnostic probabilities. Methods of Information in Medicine 17, 227-237.
14. Hilden, J., Habbema, J. D. and Bjerregaard, B. (1978b). The measurement of performance in probabilistic diagnosis. III. Methods based on continuous functions of the diagnostic probabilities. Methods of Information in Medicine 17, 238-246.
15. Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D. and Ziegler, A. (2014a). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory. Biometrical Journal 56, 534-563.
16. Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., König, I. R. and Ziegler, A. (2014b). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications. Biometrical Journal 56, 564-583.
17. Malley, D. J., Malley, K. G. and Pajevic, S. (2011). Statistical Learning for Biomedical Data. Cambridge University Press.
18. Shin, S. J., and Wu, Y. (2014). Variable selection in large margin classifier-based probability estimation with high-dimensional predictors. Biometrical Journal 56, 594-596.
19. Simon, R. (2014). Class probability estimation for medical studies. Biometrical Journal 56, 597-600.
20. Steyerberg, E. W., van der Ploeg, T. and Van Calster, B. (2014). Risk prediction with machine learning and regression methods. Biometrical Journal 56, 601-606.
21. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B 58, 267-288.
22. Ziegler, A. (2014). Rejoinder. Biometrical Journal 56, 607-613.