Methods for building linear regression equations in the “structure-property” problems
Abstract
The application of different alternative approaches for building linear regression equations in tasks which are connected with description of physicochemical parameters of molecules has been described. The Ordinary Least Squares, the Least Absolute Deviation, and the Orthogonal Distances methods are among the chosen approaches. In tasks, connected with multicollinearity of predictor sets, the principle component regression and L2-regularization have been applied. The special attention has been given to those approaches that made possible to reduce the number of predictors (the L1-regularization, the Least Angles methods). In case of data with noticeable errors in both dependent and independent variables, the orthogonal distance method has been examined as an alternative to the least square approach. The adequacy of previously investigated least absolute deviation of orthogonal distances (LADOD) method has been demonstrated.
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References
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