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• W1506427482 abstract "In this paper, we define a class of cross-validatory model selection criteria as an estimator of the predictive risk function based on a discrepancy between a candidate model and the true model. For a vector of unknown parameters, $n$ estimators are required for the definition of the class, where $n$ is the sample size. The $i$th estimator $(i=1,dots,n)$ is obtained by minimizing a weighted discrepancy function in which the $i$th observation has a weight of $1-lambda$ and others have weight of $1$. Cross-validatory model selection criteria in the class are specified by the individual $lambda$. The sample discrepancy function and the ordinary cross-validation (CV) criterion are special cases of the class. One may choose $lambda$ to minimize the biases. The optimal $lambda$ makes the bias-corrected CV (CCV) criterion a second-order unbiased estimator for the risk function, while the ordinary CV criterion is a first-order unbiased estimator of the risk function." @default.
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• W1506427482 date "2013-07-01" @default.
• W1506427482 modified "2023-10-18" @default.
• W1506427482 title "A class of cross-validatory model selection criteria" @default.
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• W1506427482 doi "https://doi.org/10.32917/hmj/1372180510" @default.
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