Subsampling inference for nonparametric extremal conditional quantiles

Daisuke Kurisu, Taisuke Otsu

This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance parameters in the limiting distribution of the local estimator. A simulation study and empirical example illustrate usefulness of our subsampling inference to investigate extremal phenomena.

MoreLess

Year of publication:	2025
Authors:	Kurisu, Daisuke ; Otsu, Taisuke
Subject:	Nichtparametrisches Verfahren \| Nonparametric statistics \| Induktive Statistik \| Statistical inference \| Schätztheorie \| Estimation theory

freely available

Full text |

More access options

doi.org

Check Google Scholar

I need help

Which error do you want to report?

Report: URL of this record is broken

Please note: Sometimes more than one URL is available for a resource. Please click on "More Options" to display these URLs

Report: Addition of an URL to that resource

Please note: Sometimes more than one URL is available for a resource. Please click on "More Options" to display these URLs

Report: The language(s) of that resource is/are…

Please note: Required language not in the list? Please use comment field

Report: Addition of an identifier to that resource

Report: report typo

Report: Add statement

Report: Add keywords

Please use the commentary box to describe other errors

Would you like to receive a feedback?

Commentary

Please type the code from the image

For general questions please use this address.

Persistent link: https://www.econbiz.de/10015374601