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Abstract:
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Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure have been less effective, due to the employment of an inappropriate sharpening transformation. In the present paper, iterated data sharpening algorithm is proposed which reduces the asymptotic bias at each iteration. The efficacy of the iterative approach is demonstrated via a simulation study. This study also shows that after iteration, the resulting kernel regressions are less sensitive to bandwidth choice, and a further simulation study demonstrates that iterated data sharpening with data-driven bandwidth selection via cross-validation can lead to more accurate regression function estimation.
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