A lack-of-fit test for quantile regression process models

Xingdong Feng, Qiaochu Liu and Caixing Wang

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

Abstract: Quantile regression is a widely used statistical tool for data analysis in practice, but model misspecifications may lead to incorrect inferences. In this paper, a lack-of-fit test for quantile regression processes is proposed for those cases with multivariate covariates, which has not been well studied in the existing literature. An asymptotic result is established, and a numerical study has demonstrated that the proposed method is promising.

Keywords: B-splines; Bahadur representation; Bootstrap; Specification test (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715222001936
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:s0167715222001936

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.2022.109680

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 ().