A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels

Kimberly F. Manago; Terri S. Hogue; Aaron Porter; Amanda S. Hering

doi:10.1016/j.spl.2018.07.023

A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels

Kimberly F. Manago, Terri S. Hogue, Aaron Porter, Amanda S. Hering

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.

Original language	English (US)
Pages (from-to)	44-51
Number of pages	8
Journal	Statistics and Probability Letters
Volume	144
DOIs	https://doi.org/10.1016/j.spl.2018.07.023
State	Published - Dec 12 2018
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-09
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: Kimberly F. Manago and Terri S. Hogue were supported, in part, by the National Science Foundation (NSF) Water Sustainability and Climate Grant (EAR-12040235) and the NSF Engineering Research Center for Reinventing the Nation's Urban Water Infrastructure (ReNUWIt.org; EEC-1028968). Amanda S. Hering was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/j.spl.2018.07.023

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title = "A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels",

abstract = "Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.",

author = "Manago, {Kimberly F.} and Hogue, {Terri S.} and Aaron Porter and Hering, {Amanda S.}",

note = "KAUST Repository Item: Exported on 2022-06-09 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 Acknowledgements: Kimberly F. Manago and Terri S. Hogue were supported, in part, by the National Science Foundation (NSF) Water Sustainability and Climate Grant (EAR-12040235) and the NSF Engineering Research Center for Reinventing the Nation's Urban Water Infrastructure (ReNUWIt.org; EEC-1028968). Amanda S. Hering was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1016/j.spl.2018.07.023",

language = "English (US)",

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journal = "Statistics and Probability Letters",

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T1 - A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels

AU - Manago, Kimberly F.

AU - Hogue, Terri S.

AU - Porter, Aaron

AU - Hering, Amanda S.

N1 - KAUST Repository Item: Exported on 2022-06-09 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 Acknowledgements: Kimberly F. Manago and Terri S. Hogue were supported, in part, by the National Science Foundation (NSF) Water Sustainability and Climate Grant (EAR-12040235) and the NSF Engineering Research Center for Reinventing the Nation's Urban Water Infrastructure (ReNUWIt.org; EEC-1028968). Amanda S. Hering was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2018/12/12

Y1 - 2018/12/12

N2 - Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.

AB - Groundwater levels in urban areas are irregularly sampled and not well understood. Using a separable space–time Bayesian Hierarchical Model, we obtain multiple imputations of the missing values to analyze spatial and temporal groundwater level fluctuations in Los Angeles, CA.

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DO - 10.1016/j.spl.2018.07.023

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EP - 51

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels

Abstract

Bibliographical note

ASJC Scopus subject areas

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