A weighted quantile regression for nonlinear models with randomly censored data

Hailin Feng and Qianqian Luo

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 18, 4167-4179

Abstract: Quantile regression has great flexibility in describing the effect of covariate on response. In this paper, based on the censored median regression proposed by Zhou, we develop a new weighted quantile regression of nonlinear models when the response is randomly censored. Our method can be applied to more complicated quantile regression models at any quantile within (0, 1), which contains censored median regression. Furthermore, the consistency and asymptotic normality of the proposed estimator are derived under appropriate assumptions. Finally, the finite sample performance of the proposed estimator is examined by numerical simulation.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1713364 (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:50:y:2021:i:18:p:4167-4179

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

DOI: 10.1080/03610926.2020.1713364

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 ().