Estimation of prediction error by using K-fold cross-validation

Statistics and Computing

Volumn 21, Issue 2, 2011, Pages 137-146

  Fushiki, Tadayoshi ^a  

^a INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

Author keywords

Bias correction; K fold cross validation; Large dataset; Prediction error

Indexed keywords

EID: 79951551258     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-009-9153-8     Document Type: Article

References (12)

1. Burman, P.: A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76, 503-514 (1989).
2. Davison, A. C., Hinkley, D. V.: Bootstrap Methods and Their Application. Cambridge University Press, Cambridge (1997).
3. Efron, B.: The estimation of prediction error: covariance penalties and cross-validation (with discussion). J. Am. Stat. Assoc. 99, 619-642 (2004).
4. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001).
5. Konishi, S., Kitagawa, G.: Information Criteria and Statistical Modeling. Springer, New York (2007).
6. L, cross-validation and generalized cross-validation: discrete index set. Ann. Stat. 15(3), 958-975 (1987).
7. Shao, J.: Linear model selection by cross-validation. J. Am. Stat. Assoc. 88, 486-494 (1993).
8. Stone, M.: Cross-validatory choice and assessment of statistical predictions (with discussion). J. R. Stat. Soc., Ser. B 36, 111-147 (1974).
9. Stone, M.: An asymptotic equivalence of choice of model by cross-validation and Akaike's criterion. J. R. Stat. Soc., Ser. B 39, 44-47 (1977).
10. van der Vaart, A. W.: Asymptotic Statistics. Cambridge University Press, Cambridge (1998).
11. Yanagihara, H., Tonda, T., Matsumoto, C.: Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition. J. Multivar. Anal. 97, 1965-1975 (2006).
12. Yang, Y.: Consistency of cross validation for comparing regression procedures. Ann. Stat. 35, 2450-2473 (2007).