Abstract Details
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Activity Number:
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76
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #313146
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Title:
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On variable bandwidth kernel density estimation
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Author(s):
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Janet Nakarmi*+ and Hailin Sang
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Companies:
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and University of Mississippi
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Keywords:
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bandwidth selection ;
mean squared error ;
variable kernel density estimation
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Abstract:
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In this paper we study the ideal variable bandwidth kernel estimator introduced by McKay (1993) and the plug-in practical version of variable bandwidth kernel estimator with two sequences of bandwidths as in Gine and Sang (2013). The dominating terms of the variance of the true estimator in the variance decomposition are separated from the other terms. Based on the exact formula of bias and these dominating terms, we develop the optimal bandwidth selection of this variable kernel estimator.
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