The application of Kriging and empirical Kriging based on the variables selected by SCAD

The commonly used approach for building a structure-activity/property relationship consists of three steps. First, one determines the descriptors for the molecular structure, then builds a metamodel by using some proper mathematical methods, and finally evaluates the meta-model. Some existing methods only can select important variables from the candidates, while most metamodels just explore linear relationships between inputs and outputs. Some techniques are useful to build more complicated relationship, but they may not be able to select important variables from a large number of variables. In this paper, we propose to screen important variables by the smoothly clipped absolute deviation (SCAD) variable selection procedure, and then apply Kriging model and empirical Kriging model for quantitative structure-activity/property relationship (QSAR/QSPR) research based on the selected important variables. We demonstrate the proposed procedure retains the virtues of both variable selection and Kriging model.

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Work Title The application of Kriging and empirical Kriging based on the variables selected by SCAD
Access
Open Access
Creators
  1. Xiao-Ling Peng
  2. Hong Yin
  3. Runze Li
  4. Kai-Tai Fang
Keyword
  1. Kriging models
  2. Empirical Kriging
  3. Penalized least squares
  4. QSPR
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Analytica Chimica Acta
Publication Date July 4, 2006
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.aca.2006.06.073
Deposited July 19, 2022

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Version 1
published

  • Created
  • Added 1-s2.0-S0003267006014061-main.pdf
  • Added Creator Xiao Ling Peng
  • Added Creator Hong Yin
  • Added Creator Runze Li
  • Added Creator Kai Tai Fang
  • Published
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • Kriging models, Empirical Kriging, Penalized least squares, QSPR
    Publication Date
    • 2006-09-25
    • 2006-07-04
  • Renamed Creator Xiao-Ling Peng Show Changes
    • Xiao Ling Peng
    • Xiao-Ling Peng
  • Renamed Creator Kai-Tai Fang Show Changes
    • Kai Tai Fang
    • Kai-Tai Fang
  • Updated