Assessing model fit by cross-validation

Journal of Chemical Information and Computer Sciences

Volumn 43, Issue 2, 2003, Pages 579-586

  Hawkins, Douglas M ^a   Basak, Subhash C ^b   Mills, Denise ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL COMPOUNDS; COMPUTATIONAL METHODS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; RELIABILITY;

CROSS-VALIDATION;

DATA REDUCTION;

EID: 0037361983     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci025626i     Document Type: Conference Paper

References (22)

1. Mosier, C. I. Problems and designs of cross-validation. Educ. Psychological Measurement 1951, 11, 5-11.
2. Lachenbruch, P. A.; Mickey, M. Estimation of error rates in discriminant analysis. Technometrics 1968, 10, 1-11.
3. Allen, D. M. The relationship between variable selection and data augmentation and a method for prediction. Technometrics 1968, 16, 125-127.
4. Geisser, S. The predictive sample reuse method with applications. J. Am. Stat. Assoc. 1975, 70, 320-328.
5. Efron, B. The Jacknife, the Bootstrap and other Resampling Plans; Society for Industrial and Applied Mathematics: 1982.
6. Stone, M. Cross-validatory choice and assessment of statistical predictions (with discussion). J. Royal Stat. Soc. 1974. B36, 111-147.
7. Li, K.-C. Asymptotic optimality of C_L and generalized cross-validation in ridge regression with application to spline smoothing. Annals Statistics 1986, 14, 1101-1112.
8. Droge, B. Asymptotic optimality of full cross-validation for selecting linear regression models. Statistics Probability Lett. 1999, 44, 351-357.
9. Shao, J. Linear model selection by cross-validation. J. Am. Stat. Assoc. 1993, 58, 486-494.
10. Piepel, G. F.; Szychowski, J. M.; Loeppky J. L. Augmenting Scheffe linear mixture models with squared and/or crossproduct terms. J. Quality Technol. 2002, 34, 297-314.
11. Golub, G. H.; Heath, M.; Wahba, G. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 1979, 21, 215-223.
12. Stone, M. Asymptotics for and against cross-validation. Biometrika 1977, 64, 29-35.
13. Basak, S. C.; Gute, B. D.; Grunwald, G. D. Use of topostructural topochemical, and geometric parameters in the prediction of vapor pressure: A hierarchical approach. J. Chem. Inf. Comput. Sci. 1997, 37, 651-655.
14. Basak S. C.; Mills. D. Quantitative Structure-Property Relationships (QSPRs) for the estimation of vapor pressure: A hierarchical approach using mathematical structural descriptors. J. Chem, Inf. Comput. Sci. 2001, 41, 692-701.
15. Hawkins, D. M.; Basak, S.; Shi, X. QSAR with few compounds and many features. J. Chem. Inf. Comput. Sci. 2001, 41, 663-670.
16. Hawkins. D. M.; Yin, X. A faster algorithm for ridge regression of reduced rank data. Computational Statistics Data Anal. 2002, 40, 253-262.
17. Frank, I. E.; Friedman, J. H. A statistical view of some chemometrics regression tools. Technometrics 1993, 35, 109-135.
18. Li, K.-C.; Duan, N. Regression analysis under link violation. Annals Statistics 1989, 17, 1009-1052.
19. 2! J. Mol. Graph. Modelling 2002, 20, 269-276.
20. Lam, R. L.; Welch, W. J.; Young, S. S. Uniform coverage designs for molecule selection. Technometrics 2002, 44, 99-109.
21. Novellino, E.; Fattorusso, S.; Greco, G. Use of comparative molecular field analysis and cluster analysis in series design. Pharm. Acta Helv. 1995, 70, 149-154.
22. Galil, Z.; Kiefer, J. Time- and space-saving computer methods, related to Mitchell's DETMAX, for finding D-optimum designs. Technometrics 1980, 22, 301-313.