25/05/2018 Computer Science Mathematics
DOI: 10.32628/IJSRSET184851 SemanticScholar ID: 115146041 MAG: 2974648777

Model Averaging Approach in Calibration Model

Publication Summary

This article deals with model averaging as an alternative regression technique for high-dimensional data especially in chemometrics where statistical approach is used to extract any information contained in a chemical dataset. Our simulation study indicated that model-averaging (MA) works better in high-correlated data than in low-correlated data. The result also designated MA with weighting procedure based on Mallows’ Cp and Jackknife criteria produce better predictions compared to Akaike information criterion (AIC)-based of weight if the candidate models are constructed by randomly grouping the covariates. Moreover, the prediction performance tent to increase along with the number of variables in a candidate model. We illustrated the methods to regress the concentration of curcuminoid in curcumin specimen as a function of their spectra determined by Fourier Transform Infra-red (FTIR) instrument.

CAER Authors

Avatar Image for Arief Gusnanto

Dr. Arief Gusnanto

University of Leeds

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