Optimal cross-validation in density estimation with the \(L^2\)-loss
Optimal cross-validation in density estimation with the \(L^2\)-loss
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The main focus is given to the so-called leave-\(p\)-out CV procedure (Lpo), where \(p\) denotes the cardinality of the test set. Closed-form expressions are settled for the Lpo estimator of the ri...
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Full title
Optimal cross-validation in density estimation with the \(L^2\)-loss
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TN_cdi_proquest_journals_2084366482
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2084366482
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E-ISSN
2331-8422
DOI
10.48550/arxiv.0811.0802
