Empirical Bayes Likelihood for Distrubition Family Based on Ranked Set Sampling

Abstract

In empirical likelihood methods, the key to constructing a likelihood function is to find a suitable weight scheme that reflects the importance of data. These weights are usually limited by some constraints. By maximizing this likelihood function, we can obtain estimates of the parameters and perform corresponding hypothesis tests, In this paper, we get the empirical Bayes likelihood test rules for distribution based on ranked set sampling. Its asymptotic optimality is obtained.