Academic Journal
Estimation methods based on ranked set sampling for the arctan uniform distribution with application
| Title: | Estimation methods based on ranked set sampling for the arctan uniform distribution with application |
|---|---|
| Authors: | Salem A. Alyami, Amal S. Hassan, İbrahim Elbatal, Naif Alotaibi, Ahmed M. Gemeay, Mohammed Elgarhy |
| Source: | AIMS Mathematics, Vol 9, Iss 4, Pp 10304-10332 (2024) |
| Publisher Information: | American Institute of Mathematical Sciences (AIMS), 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Statistics and Probability, Data set, Population, Social Sciences, Set (abstract data type), 02 engineering and technology, Estimator, 01 natural sciences, Decision Sciences, Filter (signal processing), Simple random sample, Sociology, Skew Distributions and Applications in Statistics, mean absolute relative error, QA1-939, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, ranked set sampling, Data mining, Demography, Global and Planetary Change, 4. Education, arctan uniform distribution, Statistics, Sampling (signal processing), RSS, Computer science, Programming language, FOS: Sociology, Monte Carlo method, Algorithm, Operating system, Physical Sciences, Global Drought Monitoring and Assessment, Environmental Science, Uncertainty Quantification and Sensitivity Analysis, Mean squared error, Computer vision, Reliability Analysis, Statistics, Probability and Uncertainty, Mathematics |
| Description: | The arctan uniform distribution (AUD) is a brand-new bounded distribution that may be used for modeling a variety of existing bounded real-world datasets. Ranked set sampling (RSS) is a useful technique for parameter estimation when accurate measurement of the observation is challenging and/or expensive. In the current study, the parameter estimator of the AUD is addressed based on RSS and simple random sampling (SRS) techniques. Some of the popular conventional estimating techniques are considered. The efficiency of the produced estimates is compared using a Monte Carlo simulation. It appears that the maximum product spacing method has an advantage in assessing the quality of proposed estimates based on the outcomes of our simulations for both the SRS and RSS datasets. In comparison to estimates produced from the SRS datasets, it can be seen that those from the RSS datasets are more reliable. This implies that RSS is a more effective sampling technique in terms of generating estimates with a smaller mean squared error. The benefit of the RSS design over the SRS design is further supported by real data results. |
| Document Type: | Article Other literature type |
| ISSN: | 2473-6988 |
| DOI: | 10.3934/math.2024504 |
| DOI: | 10.60692/5kc68-qx707 |
| DOI: | 10.60692/b32p5-mrk75 |
| Access URL: | https://doaj.org/article/c351d27dab34490a879fe77c36c2e4d8 |
| Accession Number: | edsair.doi.dedup.....bc7c986111e2f3c522f79f62e080357c |
| Database: | OpenAIRE |
| ISSN: | 24736988 |
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| DOI: | 10.3934/math.2024504 |