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Abstract Details
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Activity Number:
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98
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Journal of Nonparmametric Statistics
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| Abstract - #303809 |
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Title:
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Position-Based Kernel Smoothing
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Author(s):
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Winfried Stute*+
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Companies:
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University of Giessen
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Address:
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Mathematical Institute, Giessen, D-35392, Germany
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Keywords:
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Kernel smoothing ;
Density ;
Regression ;
Adaptive choice ;
Hazard
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Abstract:
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Usually kernel smoothing methods are based on a given kernel $K$. In this talk we present a discussion of techniques in density, regression and hazard function estimation when $K$ is adapted to the data and is therefore random. Proper choices, for example, lead to estimators of an unknown function $f(x)$, say, in which the location within the sample plays some role and not only the data in the neighborhood of $x$. The adaptive choice of smoothing parameters does not rely, as often in nonparametric smoothing, on asymptotic expansions of bias and variance.
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Authors who are presenting talks have a * after their name.
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