  One-sided cross-validation (OSCV) is a bandwidth selection method initially
introduced by Hart and Yi (1998) in the context of smooth regression functions.
Mart\'{\i}nez-Miranda et al. (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 implementation in the regression context.
