Pointwise Confidence Intervals in Nonparametric Regression with Heteroscedastic Error Structure

We assume a nonparametric model with heteroscedastic error structure and consider pointwise confidence intervals for the mean. We construct them by using quantiles from a CornishhFisher expansion and from the wild bootstrap distribution, with as well as without a subsequent bias correction. It turns out that pure undersmoothing, where the full smoothness is used by the initial estimator, outperforms the method with a subsequent bias correction.

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