Details:

Alternative Title:
Ann Appl Stat
Publisher's site:
http://dx.doi.org/10.1214/16-AOAS995
Personal Author:
Ludwig, Guilherme; Chu, Tingjin; Zhu, Jun; Wang, Haonan; Koehler, Kirsten;
Ludwig, Guilherme; Chu, Tingjin; Zhu, Jun; Wang, Haonan; Koehler, Kirsten; Less -
Description:
Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.
Subject:
[+]
Article
Geostatistics
Kriging
Semiparametric Methods
Spatial Statistics
Spatiotemporal Statistics

Source:
Ann Appl Stat. 11(1):139-160.;
Pubmed ID:
30100948
Pubmed Central ID:
PMC6086369
Document Type:
Journal Article;
Funding:
P2C HD047873/HD/NICHD NIH HHS/United States; R01 OH010533/OH/NIOSH CDC HHS/United States;
P2C HD047873/HD/NICHD NIH HHS/United States; R01 OH010533/OH/NIOSH CDC HHS/United States; Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:d462c95641a8829677724fd9f9df5c7d357a66598f37031ea6314772df085f7c
File Type:

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