Clusterwise multivariate regression of mixed-type panel data

Clusterwise multivariate regression of mixed-type panel 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 Clusterwise multivariate regression of mixed-type panel data Jan Vávra, Arnošt Komárek, Bettina Grün, Gertraud Malsiner-Walli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1882841/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2023 Read the published version in Statistics and Computing → Version 1 posted 8 You are reading this latest preprint version Abstract Multivariate panel data of mixed type are routinely collected in many different areas of application, often jointly with additional covariates which complicate the statistical analysis. Moreover, it is often of interest to identify unknown groups of units in a study population using such data structure, i.e., to perform clustering. In the Bayesian framework, we propose a finite mixture of multivariate generalised linear mixed effects regression models to cluster numeric, binary, ordinal and categorical panel outcomes jointly. The specification of suitable priors on the model parameters allows for convenient posterior inference based on Markov chain Monte Carlo (MCMC) sampling with data augmentation. The Bayesian approach allows to obtain both a classification of the subjects in the data and new subjects as well as cluster-specific parameter estimates. Finally, model estimation and selection of the number of data clusters are simultaneously performed when approximating the posterior for a single model using MCMC sampling without resorting to multiple model estimations. The performance of the proposed methodology is evaluated in a simulation study. Its application is illustrated on two data sets, one from a longitudinal patient study to infer prognosis groups, and a second one from the Czech part of the EU-SILC survey where households are annually interviewed to obtain insights into changes in their financial capability. Multivariate longitudinal data Mixed type outcome Generalised linear mixed model (GLMM) Model-based clustering Classification Sparse finite mixture EU-SILC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Nov, 2023 Read the published version in Statistics and Computing → Version 1 posted Editorial decision: Major revision 17 Dec, 2022 Reviews received at journal 02 Sep, 2022 Reviewers agreed at journal 31 Aug, 2022 Reviewers agreed at journal 27 Jul, 2022 Reviewers invited by journal 26 Jul, 2022 Editor assigned by journal 24 Jul, 2022 Submission checks completed at journal 22 Jul, 2022 First submitted to journal 21 Jul, 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-1882841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":123186821,"identity":"844aad4a-871d-4c8d-bde6-8be104e224c0","order_by":0,"name":"Jan Vávra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3RMWrDMBSA4ScEyvLAq4wDvoKMwUtMe5UGg6cOHjsVhayGrA6BHCOzi4YuJj2AplLwFgh0yZChspuSIcjJWIj+6WHrw7IE4HL92zAVAFTWZvRGsnsirpLcLCE98cv6JgKqJ3AmA4n3Jtkfxh+xt3qbqQI2HIG2QVFAGEoLaZ7jZYk64dupVBVoQ1gSVAKimYX4Ms8AUafQEKkQ9OsjIAtQwBO1bMxftBk54jYNT8R85USYhXg8UxSxTsQFQSv5UnSMWRz1RBhCWTxBwaPKQpg3nZNd+RCtG6q+8cWQ0fxT4zG1nlgfKf+m7gZ/f5sPrO86XHnvcrlc990Po65JlkuXoXIAAAAASUVORK5CYII=","orcid":"","institution":"Charles University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Vávra","suffix":""},{"id":123186822,"identity":"586e3f6a-1127-4bcc-8fef-bbe18015b7de","order_by":1,"name":"Arnošt Komárek","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Arnošt","middleName":"","lastName":"Komárek","suffix":""},{"id":123186823,"identity":"7e3f53e1-e7f8-4e93-88fc-3d9bb25f5425","order_by":2,"name":"Bettina Grün","email":"","orcid":"","institution":"Vienna University of Economics and Business","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bettina","middleName":"","lastName":"Grün","suffix":""},{"id":123186824,"identity":"b1ffec28-846c-4b60-8d1e-95828b450de2","order_by":3,"name":"Gertraud Malsiner-Walli","email":"","orcid":"","institution":"Vienna University of Economics and Business","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gertraud","middleName":"","lastName":"Malsiner-Walli","suffix":""}],"badges":[],"createdAt":"2022-07-21 17:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1882841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1882841/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11222-023-10304-5","type":"published","date":"2023-11-22T15:01:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24307802,"identity":"ca4fa5dc-d296-4537-931e-d9236e416eb5","added_by":"auto","created_at":"2022-07-25 19:02:04","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":792648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1882841/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clusterwise multivariate regression of mixed-type panel data","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1882841/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":"Multivariate longitudinal data, Mixed type outcome, Generalised linear mixed model (GLMM), Model-based clustering, Classification, Sparse finite mixture, EU-SILC","lastPublishedDoi":"10.21203/rs.3.rs-1882841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1882841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multivariate panel data of mixed type are routinely collected in many different areas of application, often jointly with additional covariates which complicate the statistical analysis. 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