A Bayesian spatiotemporal model of panel design data: airborne particle number concentration in Brisbane, Australia
From MaRDI portal
Publication:6626102
DOI10.1002/env.2597zbMATH Open1545.62732MaRDI QIDQ6626102
Mandana Mazaheri, Farhad Salimi, Kerrie L. Mengersen, Lidia Morawska, Samantha Low-Choy, Sam Clifford
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
semiparametric regressionBayesian inferenceair qualityspatiotemporal modellingultrafine particlesenvironmental exposureparticle number concentration
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Approximate Bayesian inference for large spatial datasets using predictive process models
- Sparse sampling: spatial design for monitoring stream networks
- A practical guide to splines
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
- Gaussian Markov Random Fields
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
Related Items (3)
Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution ⋮ A spatio-temporal model for the analysis and prediction of fine particulate matter concentration in Beijing ⋮ A projection-based Laplace approximation for spatial latent variable models
This page was built for publication: A Bayesian spatiotemporal model of panel design data: airborne particle number concentration in Brisbane, Australia