Sampling Information for Generalized Rayleigh Distribution with Application to Parameter Estimation

In the current paper, we considered the Fisher information matrix from generalized Rayleigh distribution (GR) distribution in moving extremes ranked set sampling (MERSS). The numerical results show that the ranked set sample carry more information about k and a than a simple random sample of equivalent size. In order to give more insight into the performance of MERSS with respect to (w.r.t.) simple random sampling (SRS), a modified unbiased estimator and a modified best linear unbiased estimator (BLUE) of scale and shape k and a from GR distribution in SRS and MERSS are studied. The numerical results show that the modified unbiased estimator and the modified BLUE of k and a in MERSS are significantly more efficient than the ones in SRS.