Weak consistency for the nonparametric kernel regression estimator based on negatively associated random errors

Lu Zhang, Rui Wang, Min Wang and Xuejun Wang

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 10, 3581-3598

Abstract: In this article, we mainly study the weak consistency for the Gasser and Müller (G-M) kernel estimator (a weighted kernel estimator with integral form) in a nonparametric regression model based on negatively associated random errors under some suitable conditions. In addition, a simulation to study the numerical performance of the consistency for the G-M estimator is provided.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2022.2158342 (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:53:y:2024:i:10:p:3581-3598

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

DOI: 10.1080/03610926.2022.2158342

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 ().