Pareto parameters estimation using moving extremes ranked set sampling

Wangxue Chen (), Rui Yang, Dongsen Yao and Chunxian Long
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Wangxue Chen: Jishou University
Rui Yang: Jishou University
Dongsen Yao: Jishou University
Chunxian Long: Jishou University

Statistical Papers, 2021, vol. 62, issue 3, No 6, 1195-1211

Abstract: Abstract 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 $$\theta $$ θ and $$\alpha $$ α from $$p(\theta , \alpha )$$ p ( θ , α ) . 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.

Keywords: Moving extremes ranked set sampling; Best linear unbiased estimator; Maximum likelihood estimator (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00362-019-01132-9

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