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Research Article | Open Access

Statistical inference for the Nadarajah-Haghighi distribution based on ranked set sampling with applications

Haidy A. Newer1( )Mostafa M. Mohie El-Din2Hend S. Ali2,3Isra Al-Shbeil4,5Walid Emam6
Department of Mathematics, Faculty of Education, Ain-Shams University, Cairo 11511, Egypt
Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo 11511, Egypt
Department of Mathematics, Center of Basic Science, Misr University for Science and Technology, Giza 12511, Egypt
Department of Mathematics and Statistics, University of Ottawa, Ottawa ON K1N 6N5, Canada
Department of Mathematics, Faculty of Science, The University of Jordan, Amman 11942, Jordan
Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
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Abstract

In this article, the maximum likelihood and Bayes inference methods are discussed for determining the two unknown parameters and specific lifetime parameters of the Nadarajah-Haghighi distribution, such as the survival and hazard rate functions, with the inclusion of ranked set sampling and simple random sampling. The estimated confidence intervals for the two parameters and any function of them are developed based on the Fisher-information matrix. Metropolis-Hastings algorithm and Lindley-approximation are used for generating the Bayes estimates and related highest posterior density credible ranges for the unknown parameters and reliability parameters under the presumption of conjugate gamma priors. A Monte-Carlo simulation study and a real-life data set have been used to assess the efficacy of the proposed methods.

CLC number: 2E15, 62G30, 62G32, 62M20, 62F25

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AIMS Mathematics
Pages 21572-21590

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Cite this article:
Newer HA, El-Din MMM, Ali HS, et al. Statistical inference for the Nadarajah-Haghighi distribution based on ranked set sampling with applications. AIMS Mathematics, 2023, 8(9): 21572-21590. https://doi.org/10.3934/math.20231099

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Received: 14 May 2023
Revised: 14 June 2023
Accepted: 25 June 2023
Published: 15 September 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)