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Activity Number:
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360
- Contributed Poster Presentations: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #313427
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Title:
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A Bayesian Approach to Adjust for Misclassification of Bivariate Areal Count Data with Spatial Autocorrelation
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Author(s):
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Jinjie Chen* and James Stamey and Joon Jin Song
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Companies:
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Baylor University and Department of Statistical Science, Baylor University and Baylor University
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Keywords:
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Bayesian;
misclassification;
bivariate;
spatial ;
sparse;
mixed model
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Abstract:
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Misclassification of count data biases and underestimate the uncertainty of coefficients in Poisson regression models. Meanwhile, spatial autocorrelation violates the assumption of independent errors, leading failure of many conventional statistical methods. We developed a Bayesian Spatial Poisson regression model that corrects for misclassification and accounts for spatial structure simultaneously. The full Bayesian procedure proposed here is flexible and easy to implement using Stan. We demonstrated the performance of our model via a simulation study by comparing it with multiple reduced models. In addition, we applied our model to a real-data example.
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Authors who are presenting talks have a * after their name.
