Asymptotic optimality of likelihood-based cross-validation.

@article{Laan2004AsymptoticOO,
  title={Asymptotic optimality of likelihood-based cross-validation.},
  author={Mark J van der Laan and Sandrine Dudoit and S{\"u}nd{\"u}z Keles},
  journal={Statistical applications in genetics and molecular biology},
  year={2004},
  volume={3},
  pages={Article4}
}
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d. observations from the true density among a collection of candidate density estimators. General examples are the selection of a model indexing a maximum likelihood estimator, and the selection of a bandwidth indexing a nonparametric (e.g. kernel) density estimator. In this article, we establish a finite sample result for a general class of likelihood-based cross-validation procedures (as… CONTINUE READING

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