Local linear estimate of the functional expectile regression

Ouahiba Litimein, Ali Laksaci, Boubaker Mechab and Salim Bouzebda

Statistics & Probability Letters, 2023, vol. 192, issue C

Abstract: This paper deals with the problem of the nonparametric estimation of the functional expectile regression. We use the local linear approach to construct a new estimator of the studied model. The main result of this note is the establishment of the almost complete consistency of this estimator. Our results are obtained under standard assumptions using the Bahadur representation of the conditional expectile. Some simulation studies are carried out to show the finite sample performances of the proposed estimator.

Keywords: Functional data analysis (FDA); Small ball probability; Kernel method; Conditional expectile; Local linear modeling; Almost complete (a.co.) convergence (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016771522200195X
Full text for ScienceDirect subscribers only

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:eee:stapro:v:192:y:2023:i:c:s016771522200195x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2022.109682

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().