Curve Correlation

Curve Correlation | 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 Curve Correlation Philip T. Reiss, Biplab Paul, Noemi Foa, Dror Arbiv, Thaddeus Tarpey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8031470/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Curve correlation was recently proposed as a way to measure association between time courses that are noisy and may be observed irregularly and at disparate time points. The crux of the method is basis-function smoothing of the time series, which can effectively mitigate the well-known attenuation problem for correlation estimation with noisy data. This technique is reminiscent of functional data analysis, but treats the observations, rather than the variables, as lying along a continuum. This paper provides an in-depth examination of curve correlation. Whereas in the classical setting the population correlation is the estimand and the sample correlation is its estimate, we show how the curve correlation plays both roles: it may function either as an estimand, since it is not observed directly, and as an estimate of an underlying stochastic process correlation. We contrast curve correlation with the related idea of dynamic correlation, investigate boot-strap and posterior simulation approaches to interval estimation, and derive a formula for the variance of the curve correlations arising from a bivariate Gaussian process. Illustrative examples are provided from accelerometry, meteorology, international development and election outcomes. B-splines dynamic correlation functional data analysis Gaussian process resampling Full Text Additional Declarations No competing interests reported. Supplementary Files curcorsupp.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-8031470","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545805179,"identity":"9f9b6a87-6df5-43e4-9f03-870e259ae156","order_by":0,"name":"Philip T. 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