The Estimation Of Accerated Failure Time Model With Censored Data

Interval censoring arises when the event of interest cannot be observed and it is only known to be either less or greater than the observation time. Such data often occur in the research of, for example, epidemiology, economics, medicine and sociology. The accelerated failure time model is widely used in survival analysis. The presence of censoring poses major challenges in the semi-parametric analysis. The existing semi-parametric estimators are computationally intractable. In this article we propose an unbiased transformation for the potential censored response variable, thus least square estimators of regression parameters can be gotten easily. This article was suggested by the idea of "unbiased transformation" of Zheng Zukang, the resulting estimators are consistent and asymptotically normal. Based on these, we can get the estimators for the distribution of the different type random error. Extensive simulation studies show that the asymptotic approximations are accurate in practical situations.