Ascertainment of the number of samples in the validation set in Monte Carlo cross validation and the selection of model dimension with Monte Carlo cross validation
作者： Yi Ping Du , Sumaporn Kasemsumran , Katsuhiko Maruo , Takehiro Nakagawa , Yukihiro Ozaki
DOI: 10.1016/J.CHEMOLAB.2005.07.004
关键词: Latent variable 、 Cross-validation 、 Mathematics 、 Dimension (vector space) 、 Mean squared error 、 Calibration (statistics) 、 Data set 、 Partial least squares regression 、 Observational error 、 Statistics
摘要: Monte Carlo cross validation (MCCV) is used in two data sets including 125 and 1643 near-infrared (NIR) spectra of biological samples, respectively, to ascertain the number samples left out for MCCV dimension PLS models consequently. With selected set, suitable latent variables (LV) may be chosen correctly. The results obtained show that root mean squared error calibration (RMSEC), (RMSECV) LV are sensitive when too many out. Based on this, RMSEC RMSECV suggested as criteria assist ascertainment MCCV. This method easy convenient use. For a larger more out, but will decrease if measurement level high.
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