Stress-Strength Reliability Estimation for Geometric-Exponential Model under Complete and Censored Sampling Using Classical and Modern Estimation Approaches

Stress-Strength Reliability Estimation for Geometric-Exponential Model under Complete and Censored Sampling Using Classical and Modern Estimation Approaches | 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 Stress-Strength Reliability Estimation for Geometric-Exponential Model under Complete and Censored Sampling Using Classical and Modern Estimation Approaches Amritha K Madhav, Jeevanand E. S., Sowbhagya S Prabhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8759674/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 This paper addresses the estimation of the stress-strength reliability parameter, defined as R = P(X < Y ), within a model where the stress variable X follows a Geometric distribution and the strength variable Y follows an Exponential distribution. The analysis is conducted for two prevalent data scenarios in reliability studies: complete and right-censored sampling. We derive and compare the performance of several estimation methodologies. The classical Maximum Likelihood Estimator (MLE) serves as a baseline. Its performance is contrasted with modern robust techniques, including three distinct shrinkage estimators based on a constant weight factor, a modified Thompson-type factor, and the Mehta and Srinivasan formulation. A comprehensive Monte Carlo simulation study is designed to evaluate these estimators under various conditions of sample size, true reliability, and censoring proportions. The performance is assessed based on Bias and Mean Squared Error (MSE). The results consistently demonstrate that shrinkage estimators offer substantial improvements in MSE over the traditional MLE, particularly in small to moderate sample sizes and under heavy censoring, highlighting the practical benefits of regularization in this mixed discrete-continuous reliability context. Applied Statistics Stress-Strength Reliability Exponential Distribution Geometric Distribution Robust Estimation Censoring Full Text Additional Declarations The authors declare no competing interests. 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-8759674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583981219,"identity":"b4de6494-16d9-4066-9b3d-916fc9e7ca21","order_by":0,"name":"Amritha K Madhav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJCCA4wNDHJAOoGBwYCBhwFEEtRysIHBmDQtDEAtiQ1IfPxa+KedfXj44w679O3tBx5+/FJwT4aBvXmbBD4tErfTDQ4cPJOcO+dMQrK0jEExDwPPsTK8WhhupwH90sacO4MhIUFawiCBh0EixwyvFnmIlvp0Cf4Hyb/BWuTf4NdiANFyOEFCIiFN8gPYFh78WgxBWs62HTecIfEgzZoBqIWNJ63YAp8WudtpzB8q26rlJfhzkm/++JNgz89+eOMNfFqQAE8CMyge2YhUDgLsBxh/kKB8FIyCUTAKRg4AADR3SfXXhRApAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0277-6924","institution":"Department of Statistics, Rajagiri College of Social Sciences, Kerala, India","correspondingAuthor":true,"prefix":"","firstName":"Amritha","middleName":"K","lastName":"Madhav","suffix":""},{"id":583981220,"identity":"7a07cbb9-8be8-4d6e-a5f3-26435101d470","order_by":1,"name":"Jeevanand E. 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The analysis is conducted for two prevalent data scenarios in reliability studies: complete and right-censored sampling. We derive and compare the performance of several estimation methodologies. The classical Maximum Likelihood Estimator (MLE) serves as a baseline. Its performance is contrasted with modern robust techniques, including three distinct shrinkage estimators based on a constant weight factor, a modified Thompson-type factor, and the Mehta and Srinivasan formulation. A comprehensive Monte Carlo simulation study is designed to evaluate these estimators under various conditions of sample size, true reliability, and censoring proportions. The performance is assessed based on Bias and Mean Squared Error (MSE). 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