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Activity Number: 346
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #316136 View Presentation
Title: Jackknife Empirical Likelihood for Linear Transformation Models with Censored Data
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

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.

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