Semiparametric Inference for Transformation Models via Empirical Likelihood

• Activity Number: 577
• Type: Topic Contributed
• Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Yichuan Zhao*+
• Companies: Georgia State University
• Keywords: Kaplan-Meier estimator ; martingale ; proportional hazards model ; proportional odds model ; Right censoring ; U-statistic

Abstract

Recent advances in the transformation model have made it possible to use this model for analyzing a variety of censored survival data. For inference on the regression parameter, there are semiparametric procedures based on the normal approximation. However, the accuracy of such procedures can be quite low when censoring rate is heavy. In this talk, we apply an empirical likelihood ratio method and derive its limiting distribution via the U-statistics. We obtain confidence regions for the regression parameters and compare the proposed method with the normal approximation based method in terms of coverage probability. The simulation results demonstrate that proposed empirical likelihood method overcomes the under-coverage problem substantially and outperforms the normal approximation based method. The proposed method is illustrated with a real data example.