Adaptive nonparametric regression on finite support

Balasubramaniam Natarajan and Weixing Song

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 2, 427-447

Abstract: In this paper, we propose an adaptive nonparametric regression estimation procedure when the covariate is supported over a finite interval. Unlike the classical symmetric kernel regression, the proposed kernel estimator is based on the Beta density function. Its large sample properties, including the asymptotic normality and the uniform convergence, are thoroughly investigated. Meanwhile, general data-driven guidelines for the bandwidth selection are suggested. The finite sample performance of the proposed estimator is evaluated via a simulation study and a real data application.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1749855 (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:lstaxx:v:51:y:2022:i:2:p:427-447

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1749855

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().