Efficient Method for Large-Sample Model-Free Feature Screening via Multi-Armed Bandits

Efficient Method for Large-Sample Model-Free Feature Screening via Multi-Armed Bandits | 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 Efficient Method for Large-Sample Model-Free Feature Screening via Multi-Armed Bandits Xiaxue Ouyang, Mengyu Li, Jun Yu, Cheng Meng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8766750/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract We consider the model-free feature screening in large-scale ultrahigh-dimensional data analysis. Existing feature screening methods often face substantial computational challenges when dealing with large sample sizes. To alleviate the computational burden, we propose a rank-based model-free sure independence screening method (CR-SIS) and its efficient variant, BanditCR-SIS. The CR-SIS method, based on Chatterjee’s rank correlation, is as straightforward to implement as the sure independence screening (SIS) method based on Pearson correlation introduced by Fan and Lv(2008), but it is significantly more powerful in detecting nonlinear relationships between variables. Motivated by the multi-armed bandit (MAB) problem, we reformulate the feature screening procedure to significantly reduce the computational complexity of CR-SIS. For a predictor matrix of size n × p, the computational cost of CR-SIS is O(n log(n)p), while BanditCR-SIS further reduces this to O(√n log(n)p + n log(n)). Theoretically, we establish the sure screening property for both CR-SIS and BanditCR-SIS under mild regularity conditions. Furthermore, we demonstrate the effectiveness of our methods through extensive experimental studies on both synthetic and real-world datasets. The results highlight their superior performance compared to classical screening methods, requiring significantly less computational time. Big data Variable selection Sure independence screening Chatterjee’s rank Full Text Additional Declarations No competing interests reported. Supplementary Files appendix.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 02 Feb, 2026 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-8766750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614350062,"identity":"95c06706-8a12-43c4-83a0-a0dcb16a0e14","order_by":0,"name":"Xiaxue Ouyang","email":"","orcid":"","institution":"Renmin University of China","correspondingAuthor":false,"prefix":"","firstName":"Xiaxue","middleName":"","lastName":"Ouyang","suffix":""},{"id":614350063,"identity":"4dcf964f-2208-48fe-a666-0fbd0084c17d","order_by":1,"name":"Mengyu Li","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Li","suffix":""},{"id":614350073,"identity":"4b3577fd-c5c7-4548-a65d-1297d80046d1","order_by":2,"name":"Jun Yu","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yu","suffix":""},{"id":614350074,"identity":"074a10b2-2766-4609-a370-662ec1876c11","order_by":3,"name":"Cheng Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACCRiDvQFMMTYQr4XnAMlaJBKI1MI/u/nYw685NnnykW+fSfxgsJHdcID52QO8ltw5lm4suy2t2PB2uplkD0Oa8YYDbOYG+LQYSOSYSUtuO5y4cXYamzQDw+HEDQd42CTwa8n/BtTyP3HjzGMgLf+J0ZLDJvlx24HE+RJsIC0HCGuRuJFmJs24LTlxA08as2WPQbLxzMNsZni18M9Ifib5c5td4vz2Y4w3flTYyfYdb36GVwsIMPOAXHgA7E4Ql5B6IGD8ASTkG4hQOQpGwSgYBSMTAADyiUYPgf6HKwAAAABJRU5ErkJggg==","orcid":"","institution":"Renmin University of China","correspondingAuthor":true,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2026-02-02 15:27:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8766750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8766750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093310,"identity":"f3345045-ea59-4e6e-afcb-e011cf0aef6b","added_by":"auto","created_at":"2026-04-03 11:36:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1423739,"visible":true,"origin":"","legend":"","description":"","filename":"StatCompBanditCR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766750/v1_covered_078feda5-de2d-46f8-965d-f253a6befb8f.pdf"},{"id":105937328,"identity":"6becad2a-14c1-4166-ba05-10a73488d0ab","added_by":"auto","created_at":"2026-04-01 15:12:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1146663,"visible":true,"origin":"","legend":"","description":"","filename":"appendix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766750/v1/f388efcbf32ce64e405c84b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Method for Large-Sample Model-Free Feature Screening via Multi-Armed Bandits","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":"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":"Big data, Variable selection, Sure independence screening, Chatterjee’s rank","lastPublishedDoi":"10.21203/rs.3.rs-8766750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8766750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We consider the model-free feature screening in large-scale ultrahigh-dimensional data analysis. 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Theoretically, we establish the sure screening property for both CR-SIS and BanditCR-SIS under mild regularity conditions. Furthermore, we demonstrate the effectiveness of our methods through extensive experimental studies on both synthetic and real-world datasets. 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