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Abstract In this paper, we study the asymptotic properties of the generalized cross validation (GCV) hyperparameter estimator and establish its connection with the Stein’s unbiased risk estimators (SURE) as well… Click to show full abstract
Abstract In this paper, we study the asymptotic properties of the generalized cross validation (GCV) hyperparameter estimator and establish its connection with the Stein’s unbiased risk estimators (SURE) as well as the mean squared error (MSE). It is shown that as the number of data goes to infinity, the GCV has the same asymptotic property as the SURE does and both of them converge to the best hyperparameter in the MSE sense. We illustrate the efficacy of the result by Monte Carlo simulations.
Keywords:
properties generalized;
generalized cross;
cross validation;
asymptotic properties
Journal Title: IFAC-PapersOnLine
Year Published: 2018
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Journal Title: IFAC-PapersOnLine
Year Published: 2018
Link to full text (if available)
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recommendations!