Author： ASHIS GANGOPADHYAY KIN CHEUNG
Publisher： Taylor & Francis Ltd
ISSN： 1048-5252
Source： Journal of Nonparametric Statistics, Vol.14, Iss.6, 2002-01, pp. : 655-664
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Abstract
In data driven bandwidth selection procedures for density estimation such as least squares cross validation and biased cross validation, the choice of a single global bandwidth is too restrictive. It is however reasonable to assume that the bandwidth has a distribution of its own and that locally, depending on the data, the bandwidth may differ. In this approach, the bandwidth is assigned a prior distribution in the neighborhood around the point at which the density is being estimated. Assuming that the kernel function is a proper probability distribution, a Bayesian approach is employed to come up with a posterior type distribution of the bandwidth given the data. Finally, the mean of the posterior distribution is used to select the local bandwidth.
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