Local asymptotic inference for nonparametric regression with censored survival data

Yanyan Liu, Guangcai Mao and Xingqiu Zhao

Journal of Nonparametric Statistics, 2020, vol. 32, issue 4, 1015-1028

Abstract: We consider a penalised nonparametric estimation of the relative risk function in the Cox proportional hazards model for survival data with right censoring. We derive the convergence rate, functional Bahadur representation (FBR) and local asymptotic normality of the nonparametric estimator by using reproducing kernel Hilbert space, counting process and empirical process theory. The new theoretical results fill the gap in the smoothing splines literature for nonparametric estimation in survival models. Furthermore, we construct the corresponding local confidence intervals by the bootstrap method. Extensive simulation studies are conducted to validate the proposed method and compare with the Bayesian confidence intervals, and a data example from the Stanford heart transplant study is provided for illustration.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2020.1837367 (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:gnstxx:v:32:y:2020:i:4:p:1015-1028

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2020.1837367

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().