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

6 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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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