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References in periodicals archive ?
To avoid this overfitting, we need to integrate cross validation into our method.
(1998) developed accurate calibration equations for both fatty acids by analyzing from 10 to 15 husked achenes per sample, obtaining an [r.sup.2] of 0.92 and a SECV/SD of 0.28 for both fatty acids in cross validation. In that work, the calibration set contained a similar variability for fatty acid composition to that used in the present research, except for stearic acid concentration.
Soil properties and MIR-PLS parameters for neat and KBr dilute samples Range of concentration, MIR-PLS regression coefficients ([R.sup.2]), standard errors on the cross validation (SECV), and number of factors for the neat and KBr dilute samples.
We also ran 3-fold cross validation using only the first paragraphs on the whole corpus, which was labeled solely by our heuristics.
Using the AMMI model, Gauch (1988) and Gauch and Zobel (1988) proposed a random data splitting and cross validation scheme in which some replicates of each genotype-environment combination are used for modelling and the remaining replicates are used for validation.
One of the advantages of shrinkage factors is that they are computed from the complete data set, whereas the truncated models chosen by the RMSPD criterion of the random data splitting and cross validation procedure are from a modelling subset.
Such methods involve resampling the given data set, using techniques such as the jackknife, the bootstrap and cross validation. Gauch (1988) introduced the name "predictive evaluation" when it is based on cross validation (Stone, 1974; Wold, 1978), and this is the principle underlying his proposal for selection of number of components in AMMI models.
([double dagger]) 1-VR = 1 minus the variance ratio calculated in cross validation during modified partial least squares regression.
Optimum equations were identified as those with high coefficients of determination for calibration ([R.sup.2]) and 1 - variance ratio (1 - VR), a low standard error of calibration (SEC), and a low standard error for cross validation (SECV).
Two criteria have been used to determine the optimal number of multiplicative terms to be retained in a multiplicative model: tests of hypotheses concerning the multiplicative terms and cross validation. Hypothesis test procedures which give satisfactory control of Type I error rates are the approximate F tests [F.sub.GH1], [F.sub.GH2], [F.sub.R], Simulation, and Iterated Simulation tests (Cornelius et al., 1993, 1996; Cornelius, 1993).
2 calibration and cross-validation results for in vitro dry matter digestibility (IVDMD) of grasses, showing the two equations that generated lowest standard error of cross validation (SECV) within each math transformation.
([double dagger]) VR = 1 minus the variance ratio calculated in cross validation in modified least squares regression, SECV = standard error of cross-validation.