In spatial epidemiology, when
applying Generalized Additive Models (GAMs) with a bivariate locally weighted
regression smooth over longitude and latitude, a natural hypothesis is whether
location is associated with an outcome, i.e. whether the smoothing term is
necessary. An approximate chi-square test (ACST) is available but has aninflated type I error rate. Permutation tests can provide appropriately sized
alternatives. This research evaluated powers of ACST and four permutation
tests: the conditional (CPT), fixed span (FSPT) and fixed multiple span (FMSPT)
and unconditional (UPT) permutation tests.
No comments:
Post a Comment