Name
Abstract | ||
|---|---|---|
Most models in quantitative structure and activity relationship (QSAR) research, proposed by various techniques Such as ordinary least squares regression, principal components regression, partial least squares regression, and multivariate adaptive regression splines, involve a linear parametric part and a random error part. The random errors in those models are assumed to be independently identical distributed. However, the independence assumption is not reasonable in many cases. Some dependence among errors should be considered just like Kriging. It has been Successfully used ill computer experiments for modeling. The aim of this paper is to apply Kriging models to QSAR. Our experiments show that the Kriging models can significantly improve the performances of the models obtained by many existing methods. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1021/ci049798m | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Field | DocType | Volume |
Kriging,Quantitative structure–activity relationship,Multivariate adaptive regression splines,Errors-in-variables models,Applied mathematics,Combinatorics,Principal component regression,Partial least squares regression,Ordinary least squares,Parametric statistics,Statistics,Mathematics | Journal | 44 |
Issue | ISSN | Citations |
6 | 0095-2338 | 6 |
PageRank | References | Authors |
0.60 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai-Tai Fang | 1 | 165 | 23.65 |
| Hong Yin | 2 | 30 | 3.80 |
| Yi-Zeng Liang | 3 | 133 | 11.83 |