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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...

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Detalles Bibliográficos
Autores principales: Alsuhabi, Hassan, Saber, Mohammad Mehdi, El-Raouf, M. M. Abd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
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.
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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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