title: Generalized common spatial factor model.
abstract: There are often two types of correlations in multivariate spatial data: correlations between variables measured at the same locations, and correlations of each variable across the locations. We hypothesize that these two types of correlations are caused by a common spatially correlated underlying factor. Under this hypothesis, we propose a generalized common spatial factor model. The parameters are estimated using the Bayesian method and a Markov chain Monte Carlo computing technique. Our main goals are to determine which observed variables share a common underlying spatial factor and also to predict the common spatial factor. The model is applied to county-level cancer mortality data in Minnesota to find whether there exists a common spatial factor underlying the cancer mortality throughout the state.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN 46285, USA.
authors.forename: Fujun
authors.initials: F
authors.lastname: Wang
authors.forename: Melanie M
authors.initials: MM
authors.lastname: Wall
mesh_terms: D001499:Bayes Theorem; D005163:Factor Analysis, Statistical; D006801:Humans; D008390:Markov Chains; D008910:Minnesota / Q000453:epidemiology; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000401:mortality; D012424:Rural Population