A locally adaptive process-convolution model for estimating the health impact of air pollution
DOI10.1214/18-AOAS1167zbMath1412.62163WikidataQ128989401 ScholiaQ128989401MaRDI QIDQ1728671
Publication date: 25 February 2019
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aoas/1542078055
air pollutionbivariate spatiotemporal modellingprocess-convolution modelsrespiratory medication rates
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)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Power-law models for infectious disease spread
- A locally adaptive process-convolution model for estimating the health impact of air pollution
- Estimation and extrapolation of time trends in registry data -- borrowing strength from related populations
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Bayesian correlation estimation
- Bayesian Models for Categorical Data
This page was built for publication: A locally adaptive process-convolution model for estimating the health impact of air pollution
