Maximum likelihood estimation of dependence parameter using ranked set sampling

Abstract

We study the maximum likelihood estimation of the dependence parameter of a general bivariate distribution using ranked set sampling. We compare the Fisher information about the dependence parameter in ranked set samples and simple random samples. Results are applied to the bivariate normal and bivariate extreme value distributions. In ranked set sampling with unequal allocations, we select samples using maximal Fisher information in order statistics. We study the performance of the parametric bootstrap for bivariate ranked set samples.