Maximum likelihood estimation based on ranked set sampling designs for two extensions of the Lindley distribution with uncensored and right-censored data

Cesar Augusto Taconeli () and Suely Ruiz Giolo ()
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Cesar Augusto Taconeli: Federal University of Paraná
Suely Ruiz Giolo: Federal University of Paraná

Computational Statistics, 2020, vol. 35, issue 4, No 14, 1827-1851

Abstract: Abstract Ranked set sampling (RSS) has been proved to be a cost-efficient alternative to simple random sampling (SRS). However, there are situations where some measurements are censored, which may not ensure the superiority of RSS over SRS. In this paper, the performance of the maximum likelihood estimators is examined when the data are assumed to follow a Power Lindley or a Weighted Lindley distribution, and are collected according to the original RSS or one of its two variations (the median and extreme RSS). An extensive simulation study, considering uncensored and right-censored data, and perfect and imperfect ranking, is carried out based on the two mentioned distributions in order to compare the performance of the maximum likelihood estimators from RSS-based designs with the corresponding SRS estimators. Two illustrations are presented based on real data sets. The first involves the lifetimes of aluminum specimens, while the second deals with the amount of spray mixture deposited on the leaves of apple trees.

Keywords: Monte Carlo simulation; Power Lindley distribution; Weighted Lindley distribution; Extreme ranked set sampling; Median ranked set sampling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-020-00984-2

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