Abstract
New expressions are obtained for the mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator in Gaussian kernel estimation of normal mixture densities. The use of such densities is in the same spirit as Marron and Wand (1992) and provides the same benefits. The resulting expressions are easily computable and describe the exact behavior of the estimator, thus complementing known asymptotic results for it. They reveal important information for small samples, not indicated by asymptotics. In particular, while asymptotics call for oversmoothing the estimator, undersmoothing may actually be more appropriate for small samples.
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