Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources

Biometrical Journal

Volumn 54, Issue 3, 2012, Pages 385-404

  Ancelet, Sophie ^a,f,g   Abellan, Juan J ^b,c   Del Rio Vilas, Víctor J ^d,e   Birch, Colin ^e   Richardson, Sylvia ^f  

^a AGROPARISTECH   (France)

^b Centre for Public Health Research CSISP   (Spain)

^c CIBER EPIDEMIOLOGÍA Y SALUD PÚBLICA CIBERESP   (Spain)

^d DEFRA   (United Kingdom)

^e VETERINARY LABORATORIES AGENCY   (United Kingdom)

^f IMPERIAL COLLEGE LONDON   (United Kingdom)

^g INSTITUT DE RADIOPROTECTION ET DE SÛRETÉ NUCLÉAIRE IRSN   (France)

Author keywords

Bayesian statistics; Data sparseness; Gaussian Markov random fields; Pseudo cross validatory predictive checks; Spatial epidemiology

Indexed keywords

MODEL CHECKING;

BAYESIAN; BAYESIAN STATISTICS; COMPONENT MODELING; DATA SPARSENESS; DATA-SOURCE; DISEASE SURVEILLANCE; GAUSSIAN MARKOV RANDOM FIELD; GEOGRAPHICAL VARIATIONS; PSEUDO CROSS-VALIDATORY PREDICTIVE CHECK; SPATIAL EPIDEMIOLOGY;

MARKOV PROCESSES;

EID: 84862187382     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201000106     Document Type: Article

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