Academic Thesis
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Bandwidth selection for kernel binomial regression
Hidenori Okumura
In nonparametric binomial regression, the weighted kernel estimator of the regression function and its efficient bias-adjusted version have been proposed by Okumura and Naito (2004) with consideration to differences of variances of observed response proportions at covariates. The aim of this article is to propose an effective data-based method for bandwidth selection of the bias-adjusted estimator. The proposed method is developed through three steps: the plug-in method by Ruppert et al. (1995), a scale adjustment suggested by Yang and Tschernig (1999), and an effective use of the approach discussed by Grizzel et al. (1969) for the rule-of-thumb part. Theoretical considerations on the asymptotic performance of the selected bandwidth are given under the situation where the numbers of covariates and responses observed at each covariate increase.
JOURNAL OF NONPARAMETRIC STATISTICS
TAYLOR & FRANCIS LTD
18
4-6
343
356
2006-05
Exist
Not exist
English
Research papers (academic journals)
10.1080/10485250601014230
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