The bias reduction in density estimation using a geometric extrapolated kernel estimator
نویسندگان
چکیده
منابع مشابه
The Relative Improvement of Bias Reduction in Density Estimator Using Geometric Extrapolated Kernel
One of a nonparametric procedures used to estimate densities is kernel method. In this paper, in order to reduce bias of kernel density estimation, methods such as usual kernel(UK), geometric extrapolation usual kernel(GEUK), a bias reduction kernel(BRK) and a geometric extrapolation bias reduction kernel(GEBRK) are introduced. Theoretical properties, including the selection of smoothness para...
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Sampling methods have a theoretical basis and should be operational in different forests; therefore selecting an appropriate sampling method is effective for accurate estimation of forest characteristics. The purpose of this study was to estimate the stand density (number per hectare) in Arasbaran forest using a variety of the plotless density estimators of the nearest neighbors sampling me...
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SUMMARY This paper proposes an algorithm for boosting kernel density estimates. We show that boosting is closely linked to a previously proposed method of bias reduction and indicate how it should enjoy similar properties. Numerical examples and simulations are used to illustrate the findings, and we also suggest further areas of research.
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Two classes of multiplicative bias correction (‘‘MBC’’) methods are applied to density estimation with support on [0, ∞). It is demonstrated that under sufficient smoothness of the true density, each MBC technique reduces the order of magnitude in bias, whereas the order of magnitude in variance remains unchanged. Accordingly, the mean integrated squared error of each MBC estimator achieves a f...
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ژورنال
عنوان ژورنال: Hacettepe Journal of Mathematics and Statistics
سال: 2016
ISSN: 1303-5010
DOI: 10.15672/hjms.201614922002