  Hall and Robinson (2009) proposed and analyzed the use of bagged
cross-validation to choose the bandwidth of a kernel density estimator. They
established that bagging greatly reduces the noise inherent in ordinary
cross-validation, and hence leads to a more efficient bandwidth selector. The
asymptotic theory of Hall and Robinson (2009) assumes that $N$, the number of
bagged subsamples, is $\infty$. We expand upon their theoretical results by
allowing $N$ to be finite, as it is in practice. Our results indicate an
important difference in the rate of convergence of the bagged cross-validation
bandwidth for the cases $N=\infty$ and $N<\infty$. Simulations quantify the
improvement in statistical efficiency and computational speed that can result
from using bagged cross-validation as opposed to a binned implementation of
ordinary cross-validation. The performance of thebagged bandwidth is also
illustrated on a real, very large, data set. Finally, a byproduct of our study
is the correction of errors appearing in the Hall and Robinson (2009)
expression for the asymptotic mean squared error of the bagging selector.
