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

by Hong Yin, Yi-Zeng Liang and Qinnan Hu

Journal of Data Science, v.1, no.4, 461-480

Abstract

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.

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