IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v144y2019icp44-51.html
   My bibliography  Save this article

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

Author

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

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.

Suggested Citation

  • Manago, Kimberly F. & Hogue, Terri S. & Porter, Aaron & Hering, Amanda S., 2019. "A Bayesian hierarchical model for multiple imputation of urban spatio-temporal groundwater levels," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 44-51.
    • Handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:44-51
    DOI: 10.1016/j.spl.2018.07.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715218302670
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2018.07.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Philip K. Hopke & Chuanhai Liu & Donald B. Rubin, 2001. "Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic," Biometrics, The International Biometric Society, vol. 57(1), pages 22-33, March.
    2. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marcus L. Nascimento & Kelly C. M. Gonçalves & Mario Jorge Mendonça, 2023. "Spatio-Temporal Instrumental Variables Regression with Missing Data: A Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 29-47, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maura Mezzetti, 2012. "Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 49-74, March.
    2. Eymen Errais & Dhikra Bahri, 2016. "Is Standard Deviation a Good Measure of Volatility? the Case of African Markets with Price Limits," Annals of Economics and Finance, Society for AEF, vol. 17(1), pages 145-165, May.
    3. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    4. Zhang, Weitao & Arhonditsis, George B., 2009. "A Bayesian hierarchical framework for calibrating aquatic biogeochemical models," Ecological Modelling, Elsevier, vol. 220(18), pages 2142-2161.
    5. Benjamin K. Johannsen & Elmar Mertens, 2021. "A Time‐Series Model of Interest Rates with the Effective Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(5), pages 1005-1046, August.
    6. Sarkka, Aila & Renshaw, Eric, 2006. "The analysis of marked point patterns evolving through space and time," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1698-1718, December.
    7. Thomas J Rodhouse & Kathryn M Irvine & Kerri T Vierling & Lee A Vierling, 2011. "Estimating Temporal Trend in the Presence of Spatial Complexity: A Bayesian Hierarchical Model for a Wetland Plant Population Undergoing Restoration," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-9, December.
    8. Peter Guttorp, 2003. "Environmental Statistics—A Personal View," International Statistical Review, International Statistical Institute, vol. 71(2), pages 169-179, August.
    9. Devin S. Johnson & Brian M. Brost & Mevin B. Hooten, 2022. "Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 382-400, June.
    10. Dursun Aydin & Ersin Yilmaz, 2021. "Censored Nonparametric Time-Series Analysis with Autoregressive Error Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 169-202, August.
    11. Hanna Meyer & Edzer Pebesma, 2022. "Machine learning-based global maps of ecological variables and the challenge of assessing them," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    12. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    13. Yijie Zhou & Francesca Dominici & Thomas A. Louis, 2010. "Racial disparities in risks of mortality in a sample of the US Medicare population," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 319-339, March.
    14. Huiyun Wu & Qingxia Chen & Lorraine B. Ware & Tatsuki Koyama, 2012. "A Bayesian approach for generalized linear models with explanatory biomarker measurement variables subject to detection limit: an application to acute lung injury," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1733-1747, March.
    15. Lionel Roques & Olivier Bonnefon, 2016. "Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
    16. Taghreed Alghamdi & Khalid Elgazzar & Taysseer Sharaf, 2021. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling," Future Internet, MDPI, vol. 13(9), pages 1-18, August.
    17. Sungduk Kim & Zhen Chen & Neil J. Perkins & Enrique F. Schisterman & Germaine M. Buck Louis, 2019. "A Model-Based Approach to Detection Limits in Studying Environmental Exposures and Human Fecundity," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 524-547, December.
    18. Bakian, Amanda V. & Sullivan, Kimberly A. & Paxton, Eben H., 2012. "Elucidating spatially explicit behavioral landscapes in the Willow Flycatcher," Ecological Modelling, Elsevier, vol. 232(C), pages 119-132.
    19. Bourgeois, A. & Gaba, S. & Munier-Jolain, N. & Borgy, B. & Monestiez, P. & Soubeyrand, S., 2012. "Inferring weed spatial distribution from multi-type data," Ecological Modelling, Elsevier, vol. 226(C), pages 92-98.
    20. Springborn, Michael & Sanchirico, James N., 2013. "A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 609-624.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:44-51. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.