Univariate Bayesian nonparametric binary regression with application in environmental management |
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Authors: | Song S. Qian Michael Lavine Craig A. Stow |
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Affiliation: | (1) Environmental Sciences and Resources, Portland State University, Portland, OR 97207, USA;(2) Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708, USA;(3) Nicholas School of the Environment, Duke University, Durham, NC 27708, USA |
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Abstract: | In environmental management, we often have to deal with binary response variables whose outcome dictates the course of action. This paper introduces a nonparametric Bayesian binary regression model with a single predictor variable that is more flexible than the commonly used logistic or probit models. Due to the Bayesian feature, the model can be easily used to combine observed data with our knowledge of the subject to produce site-specific results. By using three examples, this paper shows the potential application of the model in the environmental management, and its advantages in terms of flexibility in model specification, robustness to outliers, and realistic interpretation of data. |
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Keywords: | acid deposition Bayesian inference Dirichlet distribution fish response Gibbs sampler lake eutrophication PCB risk assessment salmonid |
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