High order data sharpening for density estimation

Peter Hall; Michael C. Minnotte

doi:10.1111/1467-9868.00329

High order data sharpening for density estimation

Peter Hall
, Michael C. Minnotte

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

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.

Original language	English
Pages (from-to)	141-157
Number of pages	17
Journal	Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume	64
Issue number	1
DOIs	https://doi.org/10.1111/1467-9868.00329
Publication status	Published - 2002

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KW - Bandwidth

KW - Bias reduction

KW - Kernel methods

KW - Local polynomial methods

KW - Mean-squared error

KW - Nonparametric curve estimation

KW - Transformation methods

KW - Wiggliness

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JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology

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High order data sharpening for density estimation

Abstract

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