Shrinkage strategy in stratified random sample subject to measurement error

The empirical likelihood estimation approach has been used in statistical applications. In this paper, we consider a stratified random sample subject to measurement error and with this framework, we propose a shrinkage estimation strategy that improves the performance of the maximum empirical likelihood estimator (MELE). Further, we generalize some recent findings that demonstrate the superiority of the shrinkage strategy over the MELE. Monte Carlo simulation results corroborate the established theoretical findings.