 Sieve maximum likelihood estimation for the proportional hazards model under informative censoringXuerong Chen, Tao Hu and Jianguo Sun Computational Statistics & Data Analysis, 2017, vol. 112, issue C, 224-234 Abstract: Failure time data often occur in many areas such as clinical trails, economics and medical follow-up studies, and a great deal of literature has been developed for their analysis when the censoring is noninformative. A number of methods have also been developed for the situation where the censoring may be informative. However, most of the existing procedures for the latter case apply only to limited situations or may not be stable or robust. In this paper, we present a copula model approach for regression analysis of right-censored failure time data in the presence of informative censoring. In the method, the copula model is used to describe the dependence between the failure time of interest and censoring time and for estimation, a sieve maximum likelihood estimation procedure is developed. In addition, the asymptotic properties of the proposed estimators are established and the simulation study indicates that the proposed method seems to work well in practice. An illustrative example is also provided. Keywords: Copula model; Informative censoring; Proportional hazard model; Sieve maximum likelihood estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:112:y:2017:i:c:p:224-234 DOI: 10.1016/j.csda.2017.03.006 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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