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On Bandwidth Variation in Kernel Estimates-A Square Root Law
Open accessAbstract
We consider kernel estimation of a smooth density $f$ at a point, but depart from the usual approach in admitting an adaptive dependence of the sharpness of the kernels on the underlying density. Proportionally varying the bandwidths like $f^{-1/2}$ at the contributing readings lowers the bias to a vanishing fraction of the usual value, and makes for performance seen in well-known estimators that force moment conditions on the kernel (and so sacrifice positivity of the curve estimate). Issues of equivariance and variance stabilitization are treated.
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Cited by
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An Efficient Semiparametric Estimator for Binary Response Models
Klein, R. W., & Spady, R. H. (1993) · Econometrica · doi:10.2307/2951556 · Cited by 759
Spline Smoothing: The Equivalent Variable Kernel Method
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The variable bandwidth mean shift and data-driven scale selection
Comaniciu, D., Ramesh, V., & Meer, P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937550 · Cited by 420
Metadata
- Authors
- Abramson, I. S.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1982-12-01
- Volume
- 10
- Issue
- 4
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2089349283
- Cited by
- 646
- References
- 0
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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