title: A latent process regression model for spatially correlated count data.
abstract: This paper proposes a regression model for spatially correlated count data that generalizes the work of Zeger (1988, Biometrika 75, 621-629) developed in a time-series setting. In this approach, spatial correlation is introduced through a latent process, and the marginal mean function may contain spatial trends and covariates. Generalized estimating equations are used to estimate and perform marginal inference on the spatial trend and covariate effects. The feasibility of this approach is demonstrated using an example of the distribution of neuronal cell counts in a laboratory culture dish.
authors.affiliation: National Cancer Institute, Biometry Branch, Bethesda, Maryland 20892-7354, USA.
authors.forename: L M
authors.initials: LM
authors.lastname: McShane
authors.forename: P S
authors.initials: PS
authors.lastname: Albert
authors.forename: M A
authors.initials: MA
authors.lastname: Palmatier
mesh_terms: D000818:Animals; D015982:Bias; D001699:Biometry / Q000379:methods*; D002452:Cell Count / Q000379:methods; D002478:Cells, Cultured; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D009474:Neurons / Q000166:cytology; D012044:Regression Analysis