Bayesian empirical likelihood of linear regression model with current status data

Abstract

Empirical likelihood has been widely used in survival data analysis recently. In this paper, we combine Bayesian idea with empirical likelihood and develop a Bayesian empirical likelihood method to analyze current status data based on the linear regression model. By constructing unbiased transformation of current status data, we derive an empirical log-likelihood function. The normal prior distribution and a Metro-Hastings method are presented to make Bayesian posterior inference. The theoretical properties of the estimators are proposed. Extensive simulation studies indicate that Bayesian empirical likelihood method performs much better than the empirical likelihood method in terms of coverage probability. Finally, we apply two real data to illustrate the proposed method.

Keywords:

• Current status data
• linear regression model
• Bayesian empirical likelihood
• MHadaptive package
• coverage probabilities

Additional information

Funding

This work was supported by National Natural Science Foundation of China (No. 11671054, No. 12171483).