Bandwidth choice for nonparametric classification
It is shown that, for kernel-based classification with univariate distributions and two populations, optimal bandwidth choice has a dichotomous character. If the two densities cross at just one point, where their curvatures have the same signs, then minimum Bayes risk is achieved using bandwidths which are an order of magnitude larger than those which minimize pointwise estimation error. On the other hand, if the curvature signs are different, or if there are multiple crossing points,...[Show more]
|Collections||ANU Research Publications|
|Source:||Annals of Statistics 2005, Vol. 33, No. 1, 284-306|
|01_Hall_Bandwidth_Choice_2005.pdf||195.96 kB||Adobe PDF|
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