Modeling hourly ozone concentration fields

DOI10.1214/09-AOAS318zbMath1202.62169arXiv0706.0073OpenAlexW2068388041MaRDI QIDQ614134

Yiping Dou, Nhu D. Le, James V. Zidek

Publication date: 27 December 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0706.0073

zbMATH Keywords

spatial predictionhierarchical Bayesdynamic linear modelspace-time fieldsozone

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (6)

A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling ⋮ Modeling hourly ozone concentration fields ⋮ Bayesian modeling of discrete-time point-referenced spatio-temporal data ⋮ A comparison of Bayesian models for daily ozone concentration levels ⋮ Hierarchical spatially varying coefficient and temporal dynamic process models usingspTDyn ⋮ A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration

Uses Software

• R
• EnviroStat

Cites Work

• Modeling hourly ozone concentration fields
• Bayesian forecasting and dynamic models.
• Estimating exposure response functions using ambient pollution concentrations
• Interpolation with uncertain spatial covariances: A Bayesian alternative to Kriging
• Spatially varying temperature trends in a central California estuary
• Statistical analysis of environmental space-time processes.
• Dynamic Models for Spatiotemporal Data
• A Spatiotemporal Model for Mexico City Ozone Levels
• Relating Ambient Particulate Matter Concentration Levels to Mortality Using an Exposure Simulator
• On Gibbs sampling for state space models

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