Bayesian modeling and analysis for gradients in spatiotemporal processes.

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  • Author(s): Quick H;Quick H; Banerjee S; Banerjee S; Carlin BP; Carlin BP
  • Source:
    Biometrics [Biometrics] 2015 Sep; Vol. 71 (3), pp. 575-84. Date of Electronic Publication: 2015 Apr 20.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Biometric Society Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • Publication Information:
      Publication: Alexandria Va : Biometric Society
      Original Publication: Washington.
    • Subject Terms:
      Bayes Theorem* ; Models, Statistical* ; Spatio-Temporal Analysis*; Air Pollution/*statistics & numerical data ; Fires/*statistics & numerical data ; Particulate Matter/*analysis; Air Pollution/analysis ; California ; Computer Simulation ; Data Interpretation, Statistical ; Humans ; Reproducibility of Results ; Sensitivity and Specificity
    • Abstract:
      Stochastic process models are widely employed for analyzing spatiotemporal datasets in various scientific disciplines including, but not limited to, environmental monitoring, ecological systems, forestry, hydrology, meteorology, and public health. After inferring on a spatiotemporal process for a given dataset, inferential interest may turn to estimating rates of change, or gradients, over space and time. This manuscript develops fully model-based inference on spatiotemporal gradients under continuous space, continuous time settings. Our contribution is to offer, within a flexible spatiotemporal process model setting, a framework to estimate arbitrary directional gradients over space at any given timepoint, temporal derivatives at any given spatial location and, finally, mixed spatiotemporal gradients that reflect rapid change in spatial gradients over time and vice-versa. We achieve such inference without compromising on rich and flexible spatiotemporal process models and use nonseparable covariance structures. We illustrate our methodology using a simulated data example and subsequently apply it to a dataset of daily PM2.5 concentrations in California, where the spatiotemporal gradient process reveals the effects of California's unique topography on pollution and detects the aftermath of a devastating series of wildfires.
      (© 2015, The International Biometric Society.)
    • References:
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      Biometrics. 2009 Dec;65(4):1243-53. (PMID: 19302408)
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    • Grant Information:
      RC1 GM092400 United States GM NIGMS NIH HHS; 1-RC1-GM092400-01 United States GM NIGMS NIH HHS
    • Contributed Indexing:
      Keywords: Gaussian process; Gradients; Markov chain Monte Carlo
    • Accession Number:
      0 (Particulate Matter)
    • Publication Date:
      Date Created: 20150423 Date Completed: 20160627 Latest Revision: 20190108
    • Publication Date:
      20240829
    • Accession Number:
      PMC4575262
    • Accession Number:
      10.1111/biom.12305
    • Accession Number:
      25898989