Mitigating Contamination Effects on Gamma Distribution Parameter Estimation Using Wavelet Shrinkage Techniques

Mitigating Contamination Effects on Gamma Distribution Parameter Estimation Using Wavelet Shrinkage Techniques | 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 Mitigating Contamination Effects on Gamma Distribution Parameter Estimation Using Wavelet Shrinkage Techniques Hutheyfa Hazem Taha, Taha Hussein Ali, Heyam A. A. Hayawi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6855768/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 uses the Maximum Likelihood Estimation method to investigate the impact of data contamination on the accuracy of parameter estimation for the Gamma distribution. A de-noising approach based on wavelet shrinkage has been proposed to address the limitations posed by contamination. Several types of wavelet functions were employed in combination with different threshold estimation techniques, namely Universal, Minimax, and Stein’s Unbiased Risk Estimate, applying the soft thresholding rule. The study involved simulating data sets generated from the Gamma distribution and analyzing real-life data assumed to follow the same distribution. A specialized program was developed in MATLAB to conduct these simulations and implement both the classical Maximum Likelihood Estimation method and the proposed wavelet-based de-noising techniques. The performance of the parameter estimates was compared using the Mean Squared Error criterion. The findings demonstrated that data contamination significantly affects the accuracy of parameter estimates obtained through the classical Maximum Likelihood Estimation method. In contrast, the proposed wavelet shrinkage method effectively reduced the influence of contamination and enhanced the accuracy of parameter estimation for the Gamma distribution. The study highlights the practical value of integrating wavelet-based denoising techniques into statistical estimation processes, particularly when working with contaminated datasets. Biostatistics Gamma distribution Maximum Likelihood Estimation Wavelet Shrinkage Data Contamination and Parameter Estimation 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-6855768","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468712696,"identity":"92c325b6-cd94-467c-97cf-0933ee0629d7","order_by":0,"name":"Hutheyfa Hazem Taha","email":"","orcid":"","institution":"University- Mosul","correspondingAuthor":false,"prefix":"","firstName":"Hutheyfa","middleName":"Hazem","lastName":"Taha","suffix":""},{"id":468707805,"identity":"a0407f7c-aa85-4150-b18b-9d495f266d48","order_by":1,"name":"Taha Hussein Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACHhBhwcMP5rAxMEgwMBgQ0ADWIsEj2UCqFgaDA8RqsWc/e/ADQ42EjPH5MwYMH8oOM0jOPrwBvy08eckSDMckeMxu5Bgwzjh3mEGaL62AgMNyDCQY2EBaeAyYedsOM8jx8BDwC/8b4x8M/yR4jPvPGDD/JUqLRI6ZBGObBFBZjgEzI1CLNEEtN96YWST2SfBI3EgrONhzLp1HsocNv1/Y+3OMb3z4ZmPP339444MfZdZyEmeY8YcYGCRA6QMM0MQwCkbBKBgFo4BCAAC7mzgiTBIoWAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0005-3288-4976","institution":"Salahaddin University- Erbil, Kurdistan Region, Iraq, College of Administration and Economics, Department of Statistics and Informatics.","correspondingAuthor":true,"prefix":"","firstName":"Taha","middleName":"Hussein","lastName":"Ali","suffix":""},{"id":468713941,"identity":"8d24b806-06d2-4ab9-8c5e-bb6f1c1e66bb","order_by":2,"name":"Heyam A. 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A de-noising approach based on wavelet shrinkage has been proposed to address the limitations posed by contamination. Several types of wavelet functions were employed in combination with different threshold estimation techniques, namely Universal, Minimax, and Stein\u0026rsquo;s Unbiased Risk Estimate, applying the soft thresholding rule. The study involved simulating data sets generated from the Gamma distribution and analyzing real-life data assumed to follow the same distribution. A specialized program was developed in MATLAB to conduct these simulations and implement both the classical Maximum Likelihood Estimation method and the proposed wavelet-based de-noising techniques. The performance of the parameter estimates was compared using the Mean Squared Error criterion. The findings demonstrated that data contamination significantly affects the accuracy of parameter estimates obtained through the classical Maximum Likelihood Estimation method. 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