Bayesian Rank Likelihood Estimation for Spatial Latent Trait Model

Bayesian Rank Likelihood Estimation for Spatial Latent Trait Model | 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 Bayesian Rank Likelihood Estimation for Spatial Latent Trait Model Daniel Biftu Bekalo, Anthony Kibira Wanjoya, Samuel Musili Mwalili This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4532982/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 In this study, a spatial latent trait model was developed to address the challenge of parameter estimation for ordinal response variables. The development of the model involved employing the Bayesian rank likelihood estimation method. The simulation algorithm was provided in detail, and the performance and sensitivity of the developed method were evaluated using simulation techniques. Method evaluation was conducted to identify any convergence issues in the developed method. The results showed that trace plots of all parameters (β, υ, and γ) showed good mixing and quick convergence. The potential scale reduction factor value for all parameters did not exceed one, indicating that convergence issues were not identified. Additionally, the developed method performed well, as demonstrated by the posterior predictive check, since simulated data generated from the posterior predictive distribution closely resemble the observed data. The developed method also effectively captures within-region variations and spatial correlations between the regions through the latent traits parameters. The assessment of performance included metrics such as root mean square error, mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. The results indicate that the estimates obtained from the developed method outperform the existing classical estimates. As a result, it can be concluded that the spatial latent trait model using Bayesian rank likelihood estimation is regarded as the better model. Biostatistics Statistical Theory Bayesian Rank Likelihood Markov Chain Monte Carlo Parameter Estimation Spatial Latent Traits Full Text Additional Declarations The authors declare no competing interests. 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. 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The development of the model involved employing the Bayesian rank likelihood estimation method. The simulation algorithm was provided in detail, and the performance and sensitivity of the developed method were evaluated using simulation techniques. Method evaluation was conducted to identify any convergence issues in the developed method. The results showed that trace plots of all parameters (β, υ, and γ) showed good mixing and quick convergence. The potential scale reduction factor value for all parameters did not exceed one, indicating that convergence issues were not identified. Additionally, the developed method performed well, as demonstrated by the posterior predictive check, since simulated data generated from the posterior predictive distribution closely resemble the observed data. The developed method also effectively captures within-region variations and spatial correlations between the regions through the latent traits parameters. 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