Optimizing Finite Population Mean Estimation through Stratified Ranked Set Sampling and Machine Learning: A Case Study on COVID-19 Data

Optimizing Finite Population Mean Estimation through Stratified Ranked Set Sampling and Machine Learning: A Case Study on COVID-19 Data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2025 V1 Latest version Share on Optimizing Finite Population Mean Estimation through Stratified Ranked Set Sampling and Machine Learning: A Case Study on COVID-19 Data Authors : Hameed Ali 0000-0001-6395-6032 [email protected] , Taoufik Saidani , and Oumaima Saidani Authors Info & Affiliations https://doi.org/10.22541/au.173867651.15622678/v1 282 views 104 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurate estimation of parameter is the core of data analysis in contemporary decision making in real life domain. The pursuit for reliable, cost efficient and data driven tools for data analysis is constant endeavor in statistical community. This research studies the optimization of finite population mean estimate within the framework of stratified ranked set sampling (SRSS), using auxiliary information and machine learning (ML) techniques. The emphasis is on increasing efficiency, especially in the context of complex datasets such as those resulting from COVID-19 epidemiological investigations. The primary aim of this paper is the development of efficient formulation of mean estimator of finite population under ranked set sampling using auxiliary information and stratification using machine learning methods, such as K-means clustering. This paper explores these developments to provide a robust and efficient methodology for estimating population mean in complex, real-world scenarios. The derivation of bias and mean squared error has been done to theoretically validate our methodology. To empirically validate the performance of the proposed estimators, we have used real data sets of different nature including COVID-19 as case study. For stratification using K-means clustering we have considered the COVID-19 data. through simulation study subject to varying conditions, the stability of the proposed estimators has been assessed. the finding suggests the significant contribution of our methodology to statistical community in making reliable inferences and well-informed decisions. Supplementary Material File (manuscript strss.docx) Download 515.47 KB Information & Authors Information Version history V1 Version 1 04 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords auxiliary information k-means clustering machine learning relative efficiency stratified ranked set sampling Authors Affiliations Hameed Ali 0000-0001-6395-6032 [email protected] The University of Agriculture Peshawar View all articles by this author Taoufik Saidani Northern Border University View all articles by this author Oumaima Saidani Princess Nourah bint Abdulrahman University College of Computer and Information Sciences View all articles by this author Metrics & Citations Metrics Article Usage 282 views 104 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hameed Ali, Taoufik Saidani, Oumaima Saidani. Optimizing Finite Population Mean Estimation through Stratified Ranked Set Sampling and Machine Learning: A Case Study on COVID-19 Data. Authorea . 04 February 2025. DOI: https://doi.org/10.22541/au.173867651.15622678/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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