Fisher Information in Moving Extreme Ranked Set Sampling
Ranked set sampling (RSS) provides an alternative technique to get efficient estimates compared to simple random sampling (SRS). In their study, Yao, Chen, Yang and Long (2021) derived Fisher information for the location and scale family of distributions using moving extreme ranked set sampling (MERSS), which is a variation of ranked set sampling. In this study, we derive the Fisher information of the parameters of the exponentiated family of distributions by using moving extreme ranked set sampling (MERSS). We derive maximum likelihood functions and likelihood equations. Special cases include the Fisher information for generalized rayleigh distribution and generalized exponential distribution. Using simulation, we find that the MERSS procedure provides more information than simple random sampling when estimating shape and scale parameters.
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