Template-Type: ReDIF-Paper 1.0
Title: Rates of Convergence for the Normal and the Bootstrap Approximations in Nonparametric Regression
Author-Name: R. Cao-Abad

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Keywords: Bootstrap, Kernel Smoothing, Nonparametric Regression
Abstract: This paper is concerned with the distributions used to construct 
 confidence intervals for the regression function in a nonparametric setup. 
 The rates of convergence for the normal limit, its plug in approach and the 
 wild bootstrap are compared conditionally on the explanatory variable X and 
 also unconditionally. It turns out that the wild bootstrap performs better 
 than the other approximations conditionally, but this behavior does not hold 
 in the unconditional situation.
Series: Sonderforschungsbereich 303, University of Bonn, Germany

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Creation-Date: 1989-06
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Handle: RePEc:bon:bonsfa:247