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Consistent bandwidth selection for kernel binary regression

✍ Scribed by Naomi Altman; Brenda MacGibbon


Book ID
104340529
Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
760 KB
Volume
70
Category
Article
ISSN
0378-3758

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✦ Synopsis


The use of nonparametric regression techniques for binary regression is a promising alternative to parametric methods. As in other nonparametric smoothing problems, the choice of smoothing parameter is critical to the performance of the estimator and the appearance of the resulting estimate. In this paper, we discuss the use of selection criteria based on estimates of squared prediction risk and show consistency and asymptotic normality of the selected bandwidths. The usefulness of the methods is explored on a data set and in a small simulation study.


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