TY - JOUR
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.
UR - http://hdl.handle.net/10754/678796
UR - https://linkinghub.elsevier.com/retrieve/pii/S0167715218302670
UR - http://www.scopus.com/inward/record.url?scp=85052726585&partnerID=8YFLogxK
U2 - 10.1016/j.spl.2018.07.023
DO - 10.1016/j.spl.2018.07.023
M3 - Article
SN - 0167-7152
VL - 144
SP - 44
EP - 51
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
ER -