Description
| Abstract: | We derive a class of higher-order kernels for estimation of densities and their derivatives which can be viewed as an extension of the second-order Gaussian kernel. These kernels have some attractive properties such as smoothness, manageable convolution formulae and Fourier transforms. One important application is the higher-order extension of exact mean integrated squared error calculations. The proposed kernels also have the advantage of simplifying computations of common window width selection algorithms such as least squares cross-validation. Efficiency calculations indicate that the Gaussian-based kernels perform almost as well as the optimal polynomial kernels when the order of the derivative being estimated is low. |
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| Item Description: | Offprint: Canadian journal of statistics.
Funding information taken from leaf 9. |
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| Physical Description: | 10 leaves, 1 unnumbered leaf ; 28 cm |
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| Bibliography: | Includes bibliographical references (leaves 9-10). |
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