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 journalArticlepeer-review

7 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 languageEnglish (US)
Pages (from-to)44-51
Number of pages8
JournalStatistics and Probability Letters
Volume144
DOIs
StatePublished - Dec 12 2018
Externally publishedYes

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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