Point and Interval Estimation of the Stress-Strength Reliability for discrete model based on different sampling plans

Point and Interval Estimation of the Stress-Strength Reliability for discrete model based on different sampling plans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Point and Interval Estimation of the Stress-Strength Reliability for discrete model based on different sampling plans Tenzile Erbayram, Yunus Akdoğan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4887140/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, ranked set sampling, a cheap and effective sampling method, has been used for statistical inferences as an alternative to traditional simple random sampling. Although this trendy topic has been frequently studied in continuous models, it has only been studied in parameter estimation for the discrete Weibull distribution in discrete models. Additionally, it is seen in the literature that stress-strength reliability has not been studied under ranked set sampling for discrete models. This paper discusses statistical inference for the stress-strength reliability when stress and strength are independent discrete Poisson-Ailamujia random variables under ranked set sampling. Moreover, stress-strength reliability estimators are obtained using point and bootstrap confidence interval estimation methods in simple random and ranked set sampling methods. The performances of the proposed estimators are compared using a Monte Carlo simulation. The simulation results and three real data applications show that the estimators obtained by ranked set sampling are preferable to the traditional simple random sampling in terms of efficiency. Bootstrap confidence intervals Discrete distribution Estimation Ranked set sampling Stress-strength reliability Monte Carlo simulation Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4887140","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376121437,"identity":"d7e1c16c-4fab-4c22-b020-2270ffe03a72","order_by":0,"name":"Tenzile Erbayram","email":"","orcid":"","institution":"Selçuk University: Selcuk Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Tenzile","middleName":"","lastName":"Erbayram","suffix":""},{"id":376121438,"identity":"b9f8487a-a593-4cae-a287-24609b50a2ed","order_by":1,"name":"Yunus Akdoğan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACNgYJEMXPwMB4gIEhgQGCidEi2cDAgNBygBgtBgeI1WJw/FjijQ+/7PKMzx8+cOBnWxoDP3uOAfPHPXi0nEk7bDmzL7nY7EZawsHethwGyZ43BgwHnuHRciC9TZq3hzlx2w0eg8OMbRUMBjdygFrwuMzg/HOQlvrEzf3nP4C12BPUciPtmDTPj8OJGxhyGIBachgMJAhokbzxLNlyZsPxxBk30gwO9pxL45E486zgwBk8WvjOpxne+PCnOrG///DDBz/KkuX425M3PqjAo0UBJMfYhhDgARF4NDAwyDeAyD/4lIyCUTAKRsGIBwCIWGAs1hz4IwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3520-7493","institution":"Selcuk Universitesi Fen Fakultesi","correspondingAuthor":true,"prefix":"","firstName":"Yunus","middleName":"","lastName":"Akdoğan","suffix":""}],"badges":[],"createdAt":"2024-08-09 12:37:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4887140/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4887140/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78668914,"identity":"1173ef5e-d83b-4750-a7de-003d54d00675","added_by":"auto","created_at":"2025-03-17 11:55:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":406921,"visible":true,"origin":"","legend":"","description":"","filename":"PointandIntervalEstimationoftheSSRRSSSRS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4887140/v1_covered_6463df9a-8ac6-46f3-9094-04f06b1223b0.pdf"}],"financialInterests":"","formattedTitle":"Point and Interval Estimation of the Stress-Strength Reliability for discrete model based on different sampling plans","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bootstrap confidence intervals, Discrete distribution, Estimation, Ranked set sampling, Stress-strength reliability, Monte Carlo simulation","lastPublishedDoi":"10.21203/rs.3.rs-4887140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4887140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, ranked set sampling, a cheap and effective sampling method, has been used for statistical inferences as an alternative to traditional simple random sampling. 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