Constrained least squares simplicial-simplicial regression

Constrained least squares simplicial-simplicial regression | 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Research Article Constrained least squares simplicial-simplicial regression Michail Tsagris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4706301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2025 Read the published version in Statistics and Computing → Version 1 posted 11 You are reading this latest preprint version Abstract Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The strategy behind the formulation new model is implemented to a an existing methodology, that is of the same spirit, showcasing how other similar models can be employed as well. Finally, the performance of the proposed technique and its comparison to the existing methodology takes place using simulation studies and real data examples. compositional data regression quadratic programming Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2025 Read the published version in Statistics and Computing → Version 1 posted Editorial decision: Revision requested 07 Oct, 2024 Reviews received at journal 15 Sep, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviews received at journal 23 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers invited by journal 18 Jul, 2024 Editor assigned by journal 12 Jul, 2024 Submission checks completed at journal 09 Jul, 2024 First submitted to journal 08 Jul, 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-4706301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336016199,"identity":"6dc6b622-e7d4-4ad3-ad56-15d75f472d85","order_by":0,"name":"Michail Tsagris","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYPACCQZ+4hWzQbVINpCohYHB4ACxOnTntz/8+HWPhb3xjeSDHxh+3SOsxewYj7G0zDOJxG030pIlGPuKidLCIC1xQCLB7EaOGQNjTwIxWtgf/wZqsTeeQbwWBjPJDwckGDdIALUw/CBKS46ZNcMBicQZZ54lSyQ2EKPl8PHHN38cqLPnbweG2Ic/RGgBAWYeGCuxjTgdDIw/4Mw/RGoZBaNgFIyCEQUAV7g4lTAsFqsAAAAASUVORK5CYII=","orcid":"","institution":"University of Crete","correspondingAuthor":true,"prefix":"","firstName":"Michail","middleName":"","lastName":"Tsagris","suffix":""}],"badges":[],"createdAt":"2024-07-08 14:31:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4706301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4706301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11222-024-10560-z","type":"published","date":"2025-01-10T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73693823,"identity":"246e99be-fdb4-429f-9e9e-cf782b669c2a","added_by":"auto","created_at":"2025-01-13 16:07:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1097845,"visible":true,"origin":"","legend":"","description":"","filename":"SCLS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4706301/v1_covered_9482fc8a-a580-4600-aaf7-6ea1eb64edec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Constrained least squares simplicial-simplicial regression","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"statistics-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"stco","sideBox":"Learn more about [Statistics and Computing](http://link.springer.com/journal/11222)","snPcode":"11222","submissionUrl":"https://submission.nature.com/new-submission/11222/3","title":"Statistics and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"compositional data, regression, quadratic programming","lastPublishedDoi":"10.21203/rs.3.rs-4706301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4706301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. 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