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Maximum ranked set sampling with unequal samples is a sampling procedure used to reduce the error of ranking of observations and increase the efficiency of statistical inference. It is used for maximum likelihood estimation of the location and shape parameters of the inverse Gaussian distribution. Its asymptotic efficiency is at least 1.4 times higher than those of estimators based on simple random sampling. It is useful in reliability studies and in Bayesian statistics involving the inverse Gaussian distribution.
Wang et al. (Mon,) studied this question.