Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 67, Issue 1, 2018, Pages 231-253

  Shaddick, Gavin ^a,b   Thomas, Matthew L ^b   Green, Amelia ^b   Brauer, Michael ^c   van Donkelaar, Aaron ^d   Burnett, Rick ^e   Chang, Howard H ^f   Cohen, Aaron ^g   Dingenen, Rita Van ^h   Dora, Carlos ⁱ   Gumy, Sophie ⁱ   Liu, Yang ^f   Martin, Randall ^d   Waller, Lance A ^f   West, Jason ^j   Zidek, James V ^c   Prüss Ustün, Annette ⁱ  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b UNIVERSITY OF BATH   (United Kingdom)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^d DALHOUSIE UNIVERSITY   (Canada)

^e HEALTH CANADA   (Canada)

^f EMORY UNIVERSITY   (United States)

^g Health Effects Institute   (United States)

^h JOINT RESEARCH CENTRE   (Italy)

ⁱ WORLD HEALTH ORGANIZATION   (Switzerland)

^j UNIVERSITY OF NORTH CAROLINA   (United States)

more..

Author keywords

Air pollution; Bayesian hierarchical modelling; Data fusion; Environmental health effects; Global burden of disease; Integrated nested Laplace approximations; Spatial modelling

Indexed keywords

EID: 85020394880     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/rssc.12227     Document Type: Article

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