2021
DOI: 10.1002/sta4.370
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Better nonparametric confidence intervals via robust bias correction for quantile regression

Abstract: In this article, we revisit the problem of how to construct better nonparametric confidence intervals for the conditional quantile function from an optimization perspective. We apply the fully data-driven bias correction procedure based on local polynomial smoothing to estimate the conditional quantile. To account for the effect of the estimated bias, we apply an asymptotic framework that the ratio of the bandwidth to the pilot bandwidth tends to some positive constant rather than zero as the sample size grows… Show more

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Cited by 2 publications
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References 40 publications
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