A Flexible Spatial Regression Model for Bounded Count Data with Extra Spatial Variation

A Flexible Spatial Regression Model for Bounded Count Data with Extra Spatial Variation | 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 A Flexible Spatial Regression Model for Bounded Count Data with Extra Spatial Variation Kosar Mahmood Hassan, Majid Jafari Khaledi, Esmaeil Najafi, Atefeh Saboori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7562860/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted 10 You are reading this latest preprint version Abstract ‎ In the presence of overdispersion‎, ‎the spatial beta-binomial model can be a useful tool for modeling bounded count data‎. ‎However‎, ‎in practical applications‎, ‎additional complexities such as unobserved heterogeneity‎, ‎model misspecification‎, ‎or the presence of skewness and spatial outliers often lead to greater variability than the beta binomial model can capture‎. ‎In this paper‎, ‎we propose a robust extension of this model that provides greater flexibility without increasing interpretational complexity‎. ‎The approach adapts the Beta-2-Binomial (B2B) model to spatial data by introducing a flexible shape parameter capturing excess variation in the data‎. ‎The model accommodates spatial dependence via a latent Gaussian random field‎. ‎Bayesian inference is performed using Markov Chain Monte Carlo algorithms‎. ‎Through an extensive simulation study and application to Loa loa prevalence data from Cameroon and Nigeria‎, ‎we demonstrate the robustness and improved predictive performance of the proposed model compared to existing spatial binomial approaches.‎ Geostatistics Binomial data Unobserved heterogeneity Skewness Spatial outliers Bayesian inference Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 08 Sep, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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