Statistics and Data Science Seminar
Tonglin Zhang
Purdue University
Loglinear residual tests of Moran' I autocorrelation and their applications to Kentucky breast cancer data
Abstract: Moran's I is the most widely used and the most frequently cited test statistic
in spatial statistical literature. This research bridges the permutation test of
Moran's I to the residuals of a loglinear model under the asymptotic normality
assumption. It provides the versions of Moran's I based on Pearson residuals ( )
and deviance residuals ( ) so that they can be used to test for spatial
clustering while at the same time account for potential covariates and
heterogeneous population sizes. Our simulations showed that both and are
effective to account for heterogeneous population sizes. The tests based on
and are applied to a set of loglieanr models for early stage and late-stage
breast cancer with socioeconomic and access-to-care data in Kentucky. The
results showed that socioeconomic and access-to-care variables can sufficiently
explain spatial clustering of early stage breast carcinomas, but these factors
cannot explain that for the late-stage. For this reason, we used local spatial
association terms and located four late-stage breast cancer clusters that could
not be explained. The results also confirmed our expectation that a high
screening level would be associated with a high incidence rate of early stage
disease, which in turn would reduce late-stage incidence rates.
Wednesday October 4, 2006 at 3:30 PM in SEO 512