Boiling Points Predictions Study via Dimension Reduction Methods: SIR, PCR and PLSR

10.6339/JDS.2003.01(4).177

Hong Yin；Yi-Zeng Liang；Qin-Nan Hu

Cross-validation ； dimension reduction ； partial least squares regression ； principal component regression ； sliced inverse regression

Journal of Data Science

1卷4期（2003 / 10 / 01）

461 - 480

英文

Variable selection is an important tool in QSAR. In this article, we employ three known techniques: sliced inverse regression (SIR), principal components regression (PCR) and partial least squares regression (PLSR) for models to predict the boiling points of 530 saturated hydrocarbons. With 122 topological indices as input variables our results show that these three methods have good performance and perform better than some existing methods in the literature.