Empirical Likelihood For Censored Partial Linear Model Based On Imputed Value

Abstract

This article aims at proposing a new type of empirical likelihood testing procedure based on the Wilks theorem and imputed value in censored partial linear model. The present study is mainly designed to use empirical likelihood (EL) method based on synthetic dependent data, and the result can not be applied directly due to the weights in it. In this article, a censored empirical log-likelihood ratio is introduced to tackle this problem. Particularly, we demonstrate that its limiting distribution is a standard chi-squared distribution with freedom of one. This method is used to calculate the p-value and construct the confidence interval. Some simulation studies are conducted to highlight the performance of the proposed EL method, and the results show that it performs well. Finally, an illustration is given using the Stanford Heart Transplant data.

Keywords:

• Censored data
• Confidence interval
• Empirical likelihood
• Imputed value
• Partial linear model

Mathematics Subject Classification:

• 62G08
• 62N03

Acknowledgments

This paper is supported by the National Natural Science Foundation of China (No. 11101452) and the Natural Science Foundation Project of CQ CSTC (Nos. 2012jjA00035 and 2009BB8221).