Flexible non-parametric regression models for compositional response data with zeros

Flexible non-parametric regression models for compositional response data with zeros | 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 Flexible non-parametric regression models for compositional response data with zeros Michail Tsagris, Abdulaziz Alenazi, Connie Connie Stewart This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2006067/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2023 Read the published version in Statistics and Computing → Version 1 posted 7 You are reading this latest preprint version Abstract Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k--NN regression, termed α--k--NN regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the α-transformation. Our model is further extended to the α--kernel regression by adopting the Nadaraya-Watson estimator. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they can be incorporated into the proposed models without modification. Extensive simulation studies and real-life data analyses highlight the advantage of using these non-parametric regressions for complex relationships between the compositional response data and Euclidean predictor variables. Both suggest that α--k--NN and α-kernel regressions can lead to more accurate predictions compared to current regression models which assume a, sometimes restrictive, parametric relationship with the predictor variables. In addition, the α--k--NN regression, in contrast to current regression techniques, enjoys a high computational efficiency rendering it highly attractive for use with large scale, massive, or big data. compositional data regression α–transformation k–NN algorithm kernel regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jul, 2023 Read the published version in Statistics and Computing → Version 1 posted Editorial decision: Major revision 24 May, 2023 Reviews received at journal 31 Dec, 2022 Reviewers agreed at journal 28 Oct, 2022 Reviewers invited by journal 27 Sep, 2022 Editor assigned by journal 30 Aug, 2022 Submission checks completed at journal 29 Aug, 2022 First submitted to journal 28 Aug, 2022 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. 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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-2006067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132418425,"identity":"b7c4372d-678d-4c2a-b355-544c762c2af4","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":""},{"id":132418426,"identity":"6e6df871-941b-47cf-9273-daaf2f929a9c","order_by":1,"name":"Abdulaziz Alenazi","email":"","orcid":"","institution":"Northern Border University","correspondingAuthor":false,"prefix":"","firstName":"Abdulaziz","middleName":"","lastName":"Alenazi","suffix":""},{"id":132418427,"identity":"43d4344a-1720-4b8a-8386-b2107fdbbbdd","order_by":2,"name":"Connie Connie Stewart","email":"","orcid":"","institution":"University of New Brunswick","correspondingAuthor":false,"prefix":"","firstName":"Connie","middleName":"Connie","lastName":"Stewart","suffix":""}],"badges":[],"createdAt":"2022-08-28 07:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2006067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2006067/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11222-023-10277-5","type":"published","date":"2023-07-22T21:40:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25861844,"identity":"a9d5d6f1-33ee-42ea-bf92-43fada17451a","added_by":"auto","created_at":"2022-08-30 19:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2623551,"visible":true,"origin":"","legend":"","description":"","filename":"alphakNNregressionforcompositionaldata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2006067/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flexible non-parametric regression models for compositional response data with zeros","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-2006067/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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, α–transformation, k–NN algorithm, kernel regression","lastPublishedDoi":"10.21203/rs.3.rs-2006067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2006067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCompositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. 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