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ABSTRACT Ranked set sampling (RSS) is an efficient method for estimating parameters when exact measurement of observation is difficult and/or expensive. In this paper, we provide maximum likelihood estimation of… Click to show full abstract
ABSTRACT Ranked set sampling (RSS) is an efficient method for estimating parameters when exact measurement of observation is difficult and/or expensive. In this paper, we provide maximum likelihood estimation of the shape and scale parameters concerning generalized Rayleigh distribution based on RSS and its some modifications. We compare the biases, mean squared errors and relative efficiencies of estimators in simple random sampling, RSS, extreme RSS and median RSS with different set and cycle sizes. Comparison of the mean squared errors of estimators in RSS for the case of imperfect ranking are also given. Monte Carlo simulation study is performed by using Mathematica 11.0 with 10,000 repetitions.
Journal Title: Journal of Statistical Computation and Simulation
Year Published: 2018
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