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Miranda, M. D. M.; Nielsen, J. P.; Sperlich, S. (2009)
Publisher: John Wiley and Sons
Languages: English
Types: Part of book or chapter of book
Subjects: HB
We introduce one-sided cross-validation to nonparametric kernel density estimation. The method is more stable than classical cross-validation and it has a better overall performance comparable to what we see in plug-in methods. One-sided cross-validation is a more direct date driven method than plugin methods with weaker assumptions of smoothness since it does not require a smooth pilot with consistent second derivatives. Our conclusions for one-sided kernel density cross-validation are similar to the conclusions obtained by Hart and Li (1998) when they introduced one-sided cross-validation in the regression context. An extensive simulation study conms that our one-sided cross-validation clearly outperforms the simple cross validation. We conclude with real data applications.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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