Advancing prediction in linear mixed models: a case study ongreenhouse gas emissions

Advancing prediction in linear mixed models: a case study ongreenhouse gas emissions | 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 Advancing prediction in linear mixed models: a case study ongreenhouse gas emissions Özge Kuran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6297210/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Environmental Modeling & Assessment → Version 1 posted 17 You are reading this latest preprint version Abstract The best linear unbiased estimator (BLUE) and the best linear unbiased predictor (BLUP) are used to estimate, respectively, the parameter vectors of fixed and random effects in linear mixed models. But, when multicollinearity problem is arised, alternative estimators and predictors to BLUE and BLUP are preferred because of bad variance property of BLUE. Commonly used prediction approaches are the ridge and Liu prediction under multicollinearityin linear mixed models and in this article, we suggest a new prediction approachto combat multicollinearity problem by expanding the Kibria–Lukman (KL) prediction approach in linear regression models to linear mixed models. We do comparisons between the KL estimator/predictor and several other estimators/predictors, namely BLUE/BLUP, ridge and Liu estimators/predictors by using the matrix mean square error criterion. We give the selection of the ridge biasing parameter. Lastly, to demonstrate the performance of our new suggested prediction approach, we make greenhouse gases data analysis and a Monte-Carlo simulation study. Ridge predictor Liu predictor Kibria-Lukman predictor linear mixed model mean square error. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Environmental Modeling & Assessment → Version 1 posted Editorial decision: Revision requested 08 Jun, 2025 Reviews received at journal 11 May, 2025 Reviews received at journal 05 May, 2025 Reviews received at journal 01 May, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 24 Mar, 2025 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. 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