Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations

Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations | 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 Article Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations Mir Subzar, Shahid Ahmad Wani, T. A. Raja, Amit Kumar Attri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4020069/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In many situations, when field researchers are inclined to deviate from the preselected sample plan and then to include nearby or related units in the sample, adaptive cluster sampling provides a nearby complete solution. For both rare and clustered populations, Thompson has introduced the adaptive cluster sampling (ACS) as a suitable sampling method when the data is not contaminated with outliers. So keeping this thing in mind, the present study focusses on defining adaptive ratio type regression estimators using OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. Subsequently, we have proposed regression type estimators utilizing OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. In this study we have also derived the mean square error property of both the adapted and proposed estimators. To evaluate the performance of these estimators we have used both real life and simulated Poisson clustered process data sets. Physical sciences/Materials science Physical sciences/Mathematics and computing Adaptive cluster sampling (ACS) OLS regression Huber M Generalized Robust regressions Mean square error Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Jul, 2024 Reviews received at journal 01 Jul, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 31 May, 2024 Reviews received at journal 29 Apr, 2024 Reviewers agreed at journal 29 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers invited by journal 24 Apr, 2024 Editor assigned by journal 24 Apr, 2024 Editor invited by journal 07 Apr, 2024 Submission checks completed at journal 07 Apr, 2024 First submitted to journal 06 Mar, 2024 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. 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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-4020069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288451620,"identity":"0c764ab2-f977-400f-a715-daca71af6f35","order_by":0,"name":"Mir Subzar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACHijN3gAkDCyI1yLBwHMApEWCFC0SCRCaIJCfkXv4w889dXX8M59f3fCjQIKBv707Aa8Wgxt5CYY9zw5LSNzOKbvZA3SYxJmzG/BrkcgxSOA5cECC4XZO2g0eoBYDiVz8WuRn5Bgc/HOgTkL+5pm0m3+I0cJwI8ewmecAs4TBDfZjt4myxeDMu2RmmQOHJTeeyWG7LWMgwUPQL/LtuYc/vjlQxy93/Pizm2/+2Mjxt/cScBgC8BiASWKVgwD7A1JUj4JRMApGwQgCADluSI49gL/BAAAAAElFTkSuQmCC","orcid":"","institution":"Department of statistics GDC Kulgam, J \u0026 K, India","correspondingAuthor":true,"prefix":"","firstName":"Mir","middleName":"","lastName":"Subzar","suffix":""},{"id":288451622,"identity":"1a434c4e-c4e0-4478-912c-1049c731b58f","order_by":1,"name":"Shahid Ahmad Wani","email":"","orcid":"","institution":"Symbiosis institute of technology, symbiosis international (Deemed) University, SIU, Lavale, pune, India.","correspondingAuthor":false,"prefix":"","firstName":"Shahid","middleName":"Ahmad","lastName":"Wani","suffix":""},{"id":288451624,"identity":"2fdd34e8-7034-4500-a3ca-224bd22bd00d","order_by":2,"name":"T. 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