Bias correction and uniform inference for the quantile density function
Econometrics
2022-07-20 v1
Abstract
For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain . The proposed procedures rely on the pivotality of the studentized bias-corrected estimator and known anti-concentration properties of the Gaussian approximation for its supremum.
Cite
@article{arxiv.2207.09004,
title = {Bias correction and uniform inference for the quantile density function},
author = {Grigory Franguridi},
journal= {arXiv preprint arXiv:2207.09004},
year = {2022}
}