Density derivative estimation using asymmetric kernels

Benedikt Funke and Masayuki Hirukawa

Journal of Nonparametric Statistics, 2024, vol. 36, issue 4, 994-1017

Abstract: This paper studies the problem of estimating the first-order derivative of an unknown density with support on $ \mathbb {R}_{+} $ R+ or $ \left [0,1\right ] $ [0,1]. Nonparametric density derivative estimators smoothed by the asymmetric, gamma and beta kernels are defined, and their convergence properties are explored. It is demonstrated that these estimators can attain the optimal convergence rate of the mean integrated squared error $ n^{-4/7} $ n−4/7 when the underlying density has third-order smoothness. Superior finite-sample properties of the proposed estimators are confirmed in Monte Carlo simulations, and usefulness of the estimators is illustrated in two real data examples.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2023.2291430 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:36:y:2024:i:4:p:994-1017

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2023.2291430

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().