Kernel regression for cause-specific hazard models with nonparametric covariate functions

Xiaomeng Qi and Zhangsheng Yu

Journal of Nonparametric Statistics, 2023, vol. 35, issue 3, 642-667

Abstract: We study the local kernel pseudo-partial likelihood approach for the cause-specific hazard model with nonparametric covariate functions. The derivative of the covariate function is estimated first, and the estimator of the nonparametric covariate function is then derived by integrating the derivative estimator. The consistency and pointwise asymptotic normality of the local kernel estimator for the interested failure types are obtained. Moreover, numerical studies show that the proposed kernel estimator performs well under a finite sample size. And we compare the local kernel estimator with the regression B-splines estimator. We also apply the proposed method to analyse the kidney and renal pelvis cancer data with composite endpoints.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2023.2197088 (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:35:y:2023:i:3:p:642-667

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

DOI: 10.1080/10485252.2023.2197088

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 ().