High order data sharpening for density estimation |
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| Authors: | Peter Hall & Michael C Minnotte |
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| Institution: | Australian National University, Canberra, Australia,;Utah State University, Logan, USA, and Australian National University, Canberra, Australia |
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| Abstract: | It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily high orders of bias reduction. Practical advantages of this approach, relative to competing methods, are demonstrated. They include the sheer simplicity of the estimators, which makes code for computing them particularly easy to write, very good mean-squared error performance, reduced `wiggliness' of estimates and greater robustness against undersmoothing. |
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| Keywords: | Bandwidth Bias reduction Kernel methods Local polynomial methods Mean-squared error Nonparametric curve estimation Transformation methods Wiggliness |
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