Spatio-temporal additive regression model selection for urban water demand
Autor: | Xueying Tang, Nikolay Bliznyuk, Hunter R. Merrill |
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Rok vydání: | 2019 |
Předmět: |
Environmental Engineering
Multivariate adaptive regression splines 010504 meteorology & atmospheric sciences Computer science 0208 environmental biotechnology Bayesian probability Regression analysis Computational intelligence 02 engineering and technology Overfitting 01 natural sciences 020801 environmental engineering Null (SQL) Prior probability Econometrics Environmental Chemistry Safety Risk Reliability and Quality Selection (genetic algorithm) 0105 earth and related environmental sciences General Environmental Science Water Science and Technology |
Zdroj: | Stochastic Environmental Research and Risk Assessment. 33:1075-1087 |
ISSN: | 1436-3259 1436-3240 |
Popis: | Understanding the factors influencing urban water use is critical for meeting demand and conserving resources. To analyze the relationships between urban household-level water demand and potential drivers, we develop a method for Bayesian variable selection in partially linear additive regression models, particularly suited for high-dimensional spatio-temporally dependent data. Our approach combines a spike-and-slab prior distribution with a modified version of the Bayesian group lasso to simultaneously perform selection of null, linear, and nonlinear models and to penalize regression splines to prevent overfitting. We investigate the effectiveness of the proposed method through a simulation study and provide comparisons with existing methods. We illustrate the methodology on a case study to estimate and quantify uncertainty of the associations between several environmental and demographic predictors and spatio-temporally varying household-level urban water demand in Tampa, FL. |
Databáze: | OpenAIRE |
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