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Activity Number: 260 - SPEED: Environmental Statistics Methods and Applications, Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #307658
Title: Bias Correction of Bounded Location Error in Binary Data
Author(s): Nelson Walker* and Trevor Hefley and Daniel Walsh
Companies: Kansas State University and Kansas State University and US Geological Survey
Keywords: Errors-in-variables; Logistic regression; Poisson point process; Probit regression

Binary regression models for spatial data are commonly used in disciplines such as epidemiology and ecology. Many spatially-referenced binary data sets suffer from location error, which occurs when the recorded location of an observation differs from its true location. When location error occurs, values of the covariates associated with the true spatial locations of the observations cannot be obtained. We show how a change of support (COS) can be applied to regression models for binary data to provide bias-corrected coefficient estimates when the true values of the covariates are unavailable, but the unknown locations of the observations are contained within non-overlapping arbitrarily shaped polygons. The COS accommodates spatial and non-spatial covariates and preserves the convenient interpretation of methods such as logistic and probit regression. Using a simulation experiment, we compare binary regression models with a COS to naive approaches that ignore location error. We illustrate the flexibility of the COS by modeling individual-level disease risk in a population using a binary data set, where the locations of the observations are unknown but contained within land units.

Authors who are presenting talks have a * after their name.

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