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Model Averaging Approach in Calibration Model


Deiby Tineke Salaki, Anang Kurnia, Arief Gusnanto, I Wayan Mangku, Bagus Sartono
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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.

Deiby Tineke Salaki, Anang Kurnia, Arief Gusnanto, I Wayan Mangku, Bagus Sartono

AIC, Calibration model, Curcumoid, FTIR, High-dimensional data, Jackknife ,Mallows Cp, Model averaging.

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Publication Details

Published in : Volume 4 | Issue 8 | May-June - 2018
Date of Publication Print ISSN Online ISSN
2018-06-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
189-195 IJSRSET184851   Technoscience Academy

Cite This Article

Deiby Tineke Salaki, Anang Kurnia, Arief Gusnanto, I Wayan Mangku, Bagus Sartono, "Model Averaging Approach in Calibration Model", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.189-195, May-June-2018.
URL : http://ijsrset.com/IJSRSET184851.php