title: A reproducing kernel-based spatial model in poisson regressions.
abstract: A semi-parametric spatial model for spatial dependence is proposed in Poisson regressions to study the effects of risk factors on incidence outcomes. The spatial model is constructed through an application of reproducing kernels. A Bayesian framework is proposed to infer the unknown parameters. Simulations are performed to compare the reproducing kernel-based method with several commonly used approaches in spatial modeling, including independent Gaussian and CAR models. Compared with these models, the reproducing kernel-based method is easy to implement and more flexible in terms of the ability to model various spatial dependence patterns. To further demonstrate the proposed method, two real data applications are discussed: Scottish lip cancer data and Florida smoke-related cancer data.
authors.affiliation: University of South Carolina, Columbia, SC, USA.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Zhang
authors.forename: Jianjun
authors.initials: J
authors.lastname: Gan
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015994:Incidence; D008390:Markov Chains; D016011:Normal Distribution; D016012:Poisson Distribution; D012044:Regression Analysis; D012307:Risk Factors; D062206:Spatial Analysis; D013223:Statistics as Topic / Q000379:methods*