Pareto parameters estimation using moving extremes ranked set sampling
Cost effective sampling is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming. In the current paper, a modification of ranked set sampling (RSS) called moving extremes RSS (MERSS) is considered for the estimation of the scale and shape parameters \(\theta \) and \(\alpha \) from \(p(\theta , \alpha )\). Several traditional estimators and ad hoc estimators will be studied under MERSS. The estimators under MERSS are compared to the corresponding ones under SRS. The simulation results show that the estimators under MERSS are significantly more efficient than the ones under SRS. A real data set is used for illustration.
KeywordsMoving extremes ranked set sampling Best linear unbiased estimator Maximum likelihood estimator
The authors thank the Editor in Chief, an associate editor and reviewers for their valuable comments and suggestions to improve the paper. This research was supported by National Science Foundation of China (Grant No. 11901236), Scientific Research Fund of Hunan Provincial Science and Technology Department (Grant No. 2019JJ50479), Scientific Research Fund of Hunan Provincial Education Department (Grant No. 18B322) and Fundamental Research Fund of Xiangxi Autonomous Prefecture (Grant No. 2018SF5026).
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