The Cox-Aalen model for doubly censored data

Pao-sheng Shen

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 23, 8075-8092

Abstract: Double censored data often arise in medical and epidemiological studies when observations are subject to both left censoring and right censoring. In this article, based on doubly censored data, we consider maximum likelihood estimation for the Cox-Aalen model with fixed covariates. By treating left censored observations as missing, we propose expectation-maximization (EM) algorithms for obtaining the maximum likelihood estimators (MLE) of the regression coefficients for the Cox-Aalen model. We establish the asymptotic properties of the MLE. Simulation studies show that MLE via the EM algorithms performs well.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1887241 (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:51:y:2022:i:23:p:8075-8092

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

DOI: 10.1080/03610926.2021.1887241

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 ().