title: Spatial prediction of counts and rates.
abstract: In this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor. Prediction methods are illustrated and compared through a case study based on real data and through a detailed simulation study. The paper emphasizes a better understanding of the strengths and weaknesses of each approach.
authors.affiliation: National Center for Environmental Health, Mailstop E70, Centers for Disease Control and Prevention, 1600 Clifton Road, NE, Atlanta, GA 30333, USA. cdg7@cdc.gov
authors.forename: Carol A
authors.initials: CA
authors.lastname: Gotway
authors.forename: Russell D
authors.initials: RD
authors.lastname: Wolfinger
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D016014:Linear Models; D008048:Lip Neoplasms / Q000453:epidemiology; D012606:Scotland / Q000453:epidemiology; D017062:Small-Area Analysis