Split Sample Empirical Likelihood

Abstract

We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true likelihood function. Our approach is built on empirical likelihood, a non-parametric approximation of the likelihood function. We show the asymptotic behaviors of our approach are identical to those seen in empirical likelihood. We demonstrate that our method performs comparably to empirical likelihood while significantly decreasing computational time.