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• W2032912672 abstract "By introducing the concept of ‘kernel contrasts’ as an error criterion, with a global norm, which is taken here to be the L2 norm that is usually used in nonparametric density estimation, it is possible to provide a completely data-based choice of the bandwidth, which is asymptotically equivalent to the optimal theoretical choice. The density estimate based on this data-based choice of the bandwidth has desirable properties. Monte Carlo studies and studies of real data sets show how much better this new method is over usual other methods such as unbiased cross-validation method. The technique is also extendible in a direct fashion to multivariate setting." @default.
• W2032912672 created "2016-06-24" @default.
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• W2032912672 date "2004-10-01" @default.
• W2032912672 modified "2023-09-27" @default.
• W2032912672 title "Kernel contrasts: a data-based method of choosing smoothing parameters in nonparametric density estimation" @default.
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• W2032912672 doi "https://doi.org/10.1080/10485250310001652610" @default.
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