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Bias correction and uniform inference for the quantile density function

Grigory Franguridi

arXiv 19 Jul 2022 · Econometrics

arXiv:2207.09004 · PDF · DOI · OpenAlex · Extracted main text

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 $[0,1]$. 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.

Citation extraction

19
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bahadur, R. R (1966) A note on quantiles in large samples0.7373367%
2Kiefer, J (1967) On Bahadur's representation of sample quantiles0.7373367%
3Csörgo, M., L. Horváth, and P. Deheuvels (1991) Estimating the quantile-density function, in0.64422100%
4Bloch, D. A. and J. L. Gastwirth (1968) On a simple estimate of the reciprocal of the density function0.40511100%
5Chernozhukov, V., D. Chetverikov, and K. Kato (2014) a): Anti-concentration and honest, adaptive confidence bands0.40511100%
6Csörgo, M. and P. Révész (1981) a):0.40511100%
7Csörgo, M. and P. Révész (1981) b):0.40511100%
8Falk, M (1986) On the estimation of the quantile density function0.40511100%
9Jones, M. C (1992) Estimating densities, quantiles, quantile densities and density quantiles0.40511100%
10Kim, J. and D. Pollard (1990) Cube root asymptotics0.40511100%

Showing the top 10 of 19 scored citations.