Bayesian modelling for spatially misaligned health and air pollution data through the INLA-SPDE approach-Reference-Cited by-同舟云学术

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Bayesian modelling for spatially misaligned health and air pollution data through the INLA-SPDE approach

Published:2019-06 Issue: Volume:31 Page:100353
ISSN:2211-6753
Container-title:Spatial Statistics
language:en
Short-container-title:Spatial Statistics

Author:

Cameletti Michela,Gómez-Rubio Virgilio,Blangiardo Marta

Funder

Italian Ministry for Education, University and Research

Medical Research Council, United Kingdom

Public Health England (PHE), United Kingdom

Consejería de Educación, Cultura y Deportes (JCCM, Spain)

FEDER, Spain

Ministerio de Economía y Competitividad (Spain)

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Statistics and Probability

Reference61 articles.

1. Short-term exposure to traffic-related air pollution and daily mortality in London, UK;Atkinson;J. Expo. Sci. Environ. Epidemiol.,2016

2. Sptimer: Spatio-temporal bayesian modeling using R;Bakar;J. Stat. Softw. Articles,2015

3. The use of ambient air quality modeling to estimate individual and population exposure for human health research: A case study of ozone in the Northern Georgia region of the United States;Bell;Environ. Int.,2006

4. A spatio-temporal downscaler for output from numerical models;Berrocal;J. Agric. Biol. Environ. Stat.,2010

5. Bayesian image restoration with two applications in spatial statistics;Besag;Ann. Inst. Stat. Math.,1991

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