New approach by kriging models to problems in QSAR

Journal of Chemical Information and Computer Sciences

Volumn 44, Issue 6, 2004, Pages 2106-2113

  Fang, Kai Tai ^a,b   Yin, Hong ^a,b   Liang, Yi Zeng ^c  

^a HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^b WUHAN UNIVERSITY   (China)

^c CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DESCRIBING FUNCTIONS; ERROR ANALYSIS; PERFORMANCE; REGRESSION ANALYSIS; SPLINES; TOPOLOGY;

KRIGING MODELS; MOLECULE DESCRIPTORS; PARAMETRIC ITEM; QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP (QSAR);

PROBLEM SOLVING;

EID: 10044263477     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci049798m     Document Type: Article

References (19)

1. Massy, W. F. Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 1965, 60, 234-246.
2. Wold, H. Soft modelling by latent variables: the nonlinear iterative partial least squares (NIPALS) approach. Perspect. Probability Stat. In Honor of M. S. Bartlett, 1975, 117-144.
3. Friedman, J. Multivariate adaptive regression splines (with discussion). Ann. Statistics 1991, 19(1), 1-141.
4. Friedman, J. Stochastic gradient boosting; Techniqual report; Stanford University, 1999.
5. Friedman, J. Greedy function approximation: the gradient boosting machine. Ann. Statistics 2001, 29(5).
6. Green, P.; Silverman, B. Nonparametric Regression and Generalized Linear Models; A Roughness Penalty approach; Chapman and Hall: London, 1994.
7. Wahba, G. Spline Models for Observational Data; SIAM: Philadelphia, 1990.
8. Daubechies, I. Ten Lectures in Wavelets; Society for Industrial and Applied Mathematics: Philadelphia, PA, 1992.
9. Li, K. C. Sliced inverse regression for dimension reduction. J. Am. Stat. Assoc. 1991, 86, 316-327.
10. He, P.; Fang, K. T.; Xu, C. J. The classification tree combined with SIR and its applications to classification of mass spectra. J. Data Sci. 2003, 7, 425-445.
11. Friedman, J.; Tukey, J. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. Ser. C 1974, 23, 881-889.
12. Tibshirani, R. Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 1996, 58, 267-288.
13. Rohrbaugh, R. H.; Jurs, P. C. Prediction of Gas Chromatographic Retention Indexes of Selected Olefms. Anal. Chem. 1985, 57, 2770-3.
14. Du, Y. P.; Liang, Y. Z.; Yun, D. Data Mining for Seeking an Accurate Quantitative Relationship between Molecular Structure and GC Retention Indices of Alkenes by Projection Pursuit. J. Chem. Inf. Comput. Sci. 2002, 42, 1283-1292.
15. Sacks, J.; Welch, W. J.; Mitchell, T. J.; Wynn, H. P. Design and analysis of computer experiments. Stat. Sci. 1989, 4, 409-435.
16. Rücker, G.; Rücker, C. On topological indices, boiling points, and cycloalkanes. J. Chem. Inf. Comput. Sci. 1999, 39, 788-802.
17. Cressie, N. Statistics for Spatial Data, revised edition; Wiley: New York, 1993.
18. Rivoirard, J. Introduction to Disjunctive Kriging and Nonlinear Geostatistics; Oxford University Press: Oxford, 1994.
19. Lophaven, S. N.; Nielsen, H. B.; Søndergaard, J. Aspects of the Matlab Toolbox DACE. Report IMM-REP-2002-13, Informatics and Mathematical Modelling. DTU, 2002, 44 pages.