Jackknife Empirical Likelihood for Linear Transformation Models with Censored Data

• Activity Number: 346
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Yichuan Zhao* and Hanfang Yang and Shen Liu
• Companies: Georgia State University and Renmin University of China and Renmin University of China
• Keywords: Linear transformation model ; Empirical likelihood ; Jackknife ; Coverage probability

Abstract

A class of linear transformation models with censored data was proposed by Cheng et al. (1995) as a generalization of Cox models in survival analysis. This paper develops inference procedure for regression parameters based on jackknife empirical likelihood approach. We can show that the limiting variance is not necessary to estimate and the Wilk's theorem can be obtained. Jackknife empirical likelihood benefits from the simpleness in optimization using jackknife pseudo-value. In our simulation studies, the proposed method is compared with the existing methods, such as the traditional empirical likelihood in terms of coverage probability.