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Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach
In this paper, uniformly most powerful unbiased test for testing the stress-strength model has been presented for the first time. The end of the paper is recommending a method which is appropriate for no large data where a normal asymptotic distribution is not applicable. The previous methods for in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497140/ https://www.ncbi.nlm.nih.gov/pubmed/34630557 http://dx.doi.org/10.1155/2021/8705547 |
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author | Alsuhabi, Hassan Saber, Mohammad Mehdi El-Raouf, M. M. Abd |
author_facet | Alsuhabi, Hassan Saber, Mohammad Mehdi El-Raouf, M. M. Abd |
author_sort | Alsuhabi, Hassan |
collection | PubMed |
description | In this paper, uniformly most powerful unbiased test for testing the stress-strength model has been presented for the first time. The end of the paper is recommending a method which is appropriate for no large data where a normal asymptotic distribution is not applicable. The previous methods for inference on stress-strength models use almost all the asymptotic properties of maximum likelihood estimators. The distribution of components is considered exponential and generalized logistic. A corresponding unbiased confidence interval is constructed, too. We compare presented methodology with previous methods and show the method of this paper is logically better than other methods. Interesting result is that our recommended method not only uses from small sample size but also has better result than other ones. |
format | Online Article Text |
id | pubmed-8497140 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84971402021-10-08 Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach Alsuhabi, Hassan Saber, Mohammad Mehdi El-Raouf, M. M. Abd Comput Intell Neurosci Research Article In this paper, uniformly most powerful unbiased test for testing the stress-strength model has been presented for the first time. The end of the paper is recommending a method which is appropriate for no large data where a normal asymptotic distribution is not applicable. The previous methods for inference on stress-strength models use almost all the asymptotic properties of maximum likelihood estimators. The distribution of components is considered exponential and generalized logistic. A corresponding unbiased confidence interval is constructed, too. We compare presented methodology with previous methods and show the method of this paper is logically better than other methods. Interesting result is that our recommended method not only uses from small sample size but also has better result than other ones. Hindawi 2021-09-30 /pmc/articles/PMC8497140/ /pubmed/34630557 http://dx.doi.org/10.1155/2021/8705547 Text en Copyright © 2021 Hassan Alsuhabi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Alsuhabi, Hassan Saber, Mohammad Mehdi El-Raouf, M. M. Abd Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title | Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title_full | Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title_fullStr | Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title_full_unstemmed | Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title_short | Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach |
title_sort | stress-strength parameter estimation under small sample size: a testing hypothesis approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497140/ https://www.ncbi.nlm.nih.gov/pubmed/34630557 http://dx.doi.org/10.1155/2021/8705547 |
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