Friday, 19 May 2017

Power of Permutation Tests Using Generalized Additive Models with Bivariate Smoothers

biometrics impact factor
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. 

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