Spatial‐Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure

DOI10.1111/j.1541-0420.2012.01774.xzbMath1259.62106OpenAlexW1978134315WikidataQ30524252 ScholiaQ30524252MaRDI QIDQ4911941

Peter H. Langlois, Montserrat Fuentes, Joshua L. Warren, Amy H. Herring

Publication date: 20 March 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3422613

zbMATH Keywords

Bayesian analysisair pollutionMCMCspatial modelprobit regressionpreterm birth

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (6)

Multilevel quantile function modeling with application to birth outcomes ⋮ Confounder selection via penalized credible regions ⋮ Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies ⋮ Critical window variable selection for mixtures: estimating the impact of multiple air pollutants on stillbirth ⋮ Bayesian variable selection for the Cox regression model with spatially varying coefficients with applications to Louisiana respiratory cancer data ⋮ Spatial-temporal difference equations and their application in spatial-temporal data model especially for big data

Uses Software

• spBayes

Cites Work

• Bayesian Measures of Model Complexity and Fit
• Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
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