Accuracy of Binned Kernel Functional Approximations

W. Gonzalez-Manteiga, C. Sanchez-Sellero and M.P. Wand

Statistics Working Paper from Australian Graduate School of Management

Abstract: Virtually all common bandwidth selection algorithms are based on a certain type of kernel functional estimator. Such estimators can be very computationally expensive, so in practice they are often replaced by fast binned approximations. This is especially worthwhile when the bandwidth selection method involves iteration. Results for the accuracy of these approximations are derived and then used to provide an understanding of the number of binning grid points required to achieve a given level of accuracy Our results apply to both univariate and multivariate settings. Multivariate contexts are of particular interest since the cost due to having a higher number of grid points can be quite significant.

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