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A new approach to parameter estimation in ranked set sampling

M. Mahdizadeha, M. Tamandi


This articles deals with estimation using maximum product of spacings in ranked set sampling. It serves as an alternate to the traditional maximum likelihood estimation. Monte Carlo simulations are used to assess the two approaches from bias and mean squared error perspectives. The estimators are also compared with their analogs in simple random sampling. A real data set is analyzed for illustration.


Likelihood-based inference, Parameter estimation, Spacings

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