Variable selection via nonconcave penalty function in structure-boiling points correlations

A new variable selection approach based on nonconcave penalized least squares is employed for interpretation and prediction of boiling points (bps) of 530 alkanes. The good performances of the proposed method, compared with stepwise regression and the improved least absolute shrinkage and selection operator (LASSO), along with its simplicity and fast speed, makes it a valid competitor to the existing variable selection methods. All the 530 saturated hydrocarbons with carbon numbers from 2 to 10 and 128 common topological indices are taken into account. As a result, only 12 topological indices (TIs) are selected from 95 pretreated ones but they still present a satisfying fitting and prediction effects.