Estimation methods based on ranked set sampling for the power logarithmic distribution.
Average squared absolute error
Minimum spacing Linex distance
Minimum spacing square log distance
Power logarithmic distribution
Ranked set sampling
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 Jul 2024
31 Jul 2024
Historique:
received:
14
04
2024
accepted:
15
07
2024
medline:
1
8
2024
pubmed:
1
8
2024
entrez:
31
7
2024
Statut:
epublish
Résumé
The sample strategy employed in statistical parameter estimation issues has a major impact on the accuracy of the parameter estimates. Ranked set sampling (RSS) is a highly helpful technique for gathering data when it is difficult or impossible to quantify the units in a population. A bounded power logarithmic distribution (PLD) has been proposed recently, and it may be used to describe many real-world bounded data sets. In the current work, the three parameters of the PLD are estimated using the RSS technique. A number of conventional estimators using maximum likelihood, minimum spacing absolute log-distance, minimum spacing square distance, Anderson-Darling, minimum spacing absolute distance, maximum product of spacings, least squares, Cramer-von-Mises, minimum spacing square log distance, and minimum spacing Linex distance are investigated. The different estimates via RSS are compared with their simple random sampling (SRS) counterparts. We found that the maximum product spacing estimate appears to be the best option based on our simulation results for the SRS and RSS data sets. Estimates generated from SRS data sets are less efficient than those derived from RSS data sets. The usefulness of the RSS estimators is also investigated by means of a real data example.
Identifiants
pubmed: 39085318
doi: 10.1038/s41598-024-67693-4
pii: 10.1038/s41598-024-67693-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17652Informations de copyright
© 2024. The Author(s).
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