One-sided cross-validation for nonsmooth density functions

Olga Y. Savchuk ()
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Olga Y. Savchuk: University of South Florida

Computational Statistics, 2020, vol. 35, issue 3, No 14, 1253-1272

Abstract: Abstract One-sided cross-validation (OSCV) is a bandwidth selection method initially introduced by Hart and Yi (J Am Stat Assoc 93(442):620–631, 1998) in the context of smooth regression functions. Martínez-Miranda et al. (in Gregoriou (ed) Operational risk towards basel III: best practices and issues in modeling, management and regulation, Wiley, Hoboken, 2009) developed a version of OSCV for smooth density functions. This article extends the method for nonsmooth densities. It also introduces the fully robust OSCV modification that produces consistent OSCV bandwidths for both smooth and nonsmooth cases. Practical implementations of the OSCV method for smooth and nonsmooth densities are discussed. One of the considered cross-validation kernels has potential for improving the OSCV method’s performance in the regression context.

Keywords: Kernel density estimation; Cross-validation; One-sided cross-validation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00180-019-00938-3

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