Semiparametric analysis of transformation models with doubly censored data

Pao-Sheng Shen

Journal of Applied Statistics, 2011, vol. 38, issue 4, 675-682

Abstract: Double censoring arises when T represents an outcome variable that can only be accurately measured within a certain range, [ L, U ], where L and U are the left- and right-censoring variables, respectively. In this note, using Martingale arguments of Chen et al. [3], we propose an estimator (denoted by ˜β) for estimating regression coefficients of transformation model when L is always observed. Under Cox proportional hazards model, the proposed estimator is equivalent to the partial likelihood estimator for left-truncated and right-censored data if the left-censoring variables L were regarded as left-truncated variables. In this case, the estimator ˜β can be obtained by the standard software. A simulation study is conducted to investigate the performance of ˜β. For the purpose of comparison, the simulation study also includes the estimator proposed by Cai and Cheng [2] for the case when L and U are always observed.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664760903563635 (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:japsta:v:38:y:2011:i:4:p:675-682

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760903563635

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

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