An improved robust modeling of multimodal for heavy tail data

An improved robust modeling of multimodal for heavy tail data | 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PDF Research Article An improved robust modeling of multimodal for heavy tail data Weisan Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4187424/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract People often use finite mixing models to fit data with complex structures, but when faced with heavy tail data, the prediction accuracy is often inaccurate or the model component number is given incorrectly. In this paper, we design a semi-supervised mixture skew t distribution model for data with heavy tail structure, which can give ideal prediction accuracy for some data with labels and others without labels. Our model is more robust than the skew t distribution model, and we apply Monte Carlo sampling technique to the EM algorithm to solve the problem of expensive sampling. Finally, we give the simulation results. Compared with the skew t distribution model and the hybrid Gaussian model, our model has more robust fitting effect and smaller model prediction error. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 30 Mar, 2024 Submission checks completed at journal 30 Mar, 2024 First submitted to journal 29 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. 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-4187424","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285683209,"identity":"d3ff130d-3e23-4a6f-b431-e00ddd9b5a74","order_by":0,"name":"Weisan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFACxgYIzd7G8AHCSiBWC88xxhlEaoEBiTQitRgcb27+8HMHQ+KGm88Sm3l3bGPgZ88xYPxRg0fLmYNtkr1ngFpupx1s5j1zm0Gy540BM88x3FrMbiS2MfC2MeRuuJ3e/pi37TaDwY0cA2YGNjxa7j9s/vgXpOXm8cZmkBb7GyCH/cNnC2ODNNiWG2wHwVoMJHIMQPbiBPZnEtukZdsk6meeSUtsnHvmNo/EmWcFh3n7cGuRbD/++OPbNhtjvuPHDBve7rgtx9+evPHhj2+4tUCBBIQCRisPiD5AUAMcwFLCKBgFo2AUjAJkAABTPVmA8VmcjgAAAABJRU5ErkJggg==","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":true,"prefix":"","firstName":"Weisan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-03-29 10:43:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4187424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4187424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54049177,"identity":"2112f91c-955c-4aee-a97a-db7bcfddbbb7","added_by":"auto","created_at":"2024-04-03 20:35:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":184785,"visible":true,"origin":"","legend":"","description":"","filename":"Animprovedrobustmodelingofmultimodalforheavytaildata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4187424/v1_covered_2e4a7b94-e2b0-46d1-b0eb-5411a3c88f3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An improved robust modeling of multimodal for heavy tail data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-journal-of-mathematics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejma","sideBox":"Learn more about [European Journal of Mathematics](http://link.springer.com/journal/volumesAndIssues/40879)","snPcode":"40879","submissionUrl":"https://submission.nature.com/new-submission/40879/3","title":"European Journal of Mathematics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4187424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4187424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"People often use finite mixing models to fit data with complex structures, but when faced with heavy tail data, the prediction accuracy is often inaccurate or the model component number is given incorrectly. 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