Misaligned Workplace Data

Details

• Alternative Title:
  Technometrics
• Personal Author:
  Monteiro, João V. D. ; Banerjee, Sudipto ; Ramachandran, Gurumurthy
• Description:
  In industrial hygiene, a worker's exposure to chemical, physical, and biological agents is increasingly being modeled using deterministic physical models that study exposures near and farther away from a contaminant source. However, predicting exposure in the workplace is challenging and simply regressing on a physical model may prove ineffective due to biases and extraneous variability. A further complication is that data from the workplace are usually |. This means that not all timepoints measure concentrations near and far from the source. We recognize these challenges and outline a flexible Bayesian hierarchical framework to synthesize the physical model with the field data. We reckon that the physical model, by itself, is inadequate for enhanced inferential and predictive performance and deploy (multivariate) Gaussian processes to capture uncertainties and associations. We propose rich covariance structures for multiple outcomes using latent stochastic processes. This article has supplementary material available online.
• Subjects:
  Article Bayesian Melding Cross-covariances Gaussian Processes Linear Ordinary Differential Equations Markov Chain Monte Carlo Occupational Exposure Models
• Source:
  Technometrics. 56(2)
• Pubmed ID:
  32336793
• Pubmed Central ID:
  PMC7180385
• Document Type:
  Journal Article
• Funding:
  R01 OH010093/OH/NIOSH CDC HHS/United States
• Volume:
  56
• Issue:
  2
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:17c296ec99c2bcda810bda36ea099152665006d6301e35e323e2bb9a70f29f5b
• Download URL:
  https://stacks.cdc.gov/view/cdc/87421/cdc_87421_DS1.pdf
• File Type:
  [PDF - 486.62 KB ]