Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme Article Swipe
Usman Shahzad
,
Ishfaq Ahmad
,
Ibrahim M. Almanjahie
,
Amer Ibrahim Al‐Omari
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0276514
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0276514
Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.
Related Topics
Concepts
Estimator
RSS
Statistics
Computer science
Sampling (signal processing)
Rank (graph theory)
Monte Carlo method
Data set
Variable (mathematics)
Set (abstract data type)
Sorting
Sampling scheme
Mathematics
Algorithm
Operating system
Programming language
Computer vision
Combinatorics
Mathematical analysis
Filter (signal processing)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0276514
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0276514&type=printable
- OA Status
- gold
- Cited By
- 8
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307171295
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307171295Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0276514Digital Object Identifier
- Title
-
Three-fold utilization of supplementary information for mean estimation under median ranked set sampling schemeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-24Full publication date if available
- Authors
-
Usman Shahzad, Ishfaq Ahmad, Ibrahim M. Almanjahie, Amer Ibrahim Al‐OmariList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0276514Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0276514&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0276514&type=printableDirect OA link when available
- Concepts
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Estimator, RSS, Statistics, Computer science, Sampling (signal processing), Rank (graph theory), Monte Carlo method, Data set, Variable (mathematics), Set (abstract data type), Sorting, Sampling scheme, Mathematics, Algorithm, Operating system, Programming language, Computer vision, Combinatorics, Mathematical analysis, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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