title: Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.
abstract: We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University.
authors.affiliation: Department of Statistics, Stanford University, Stanford, California, 94305, U.S.A.
authors.forename: Júlia
authors.initials: J
authors.lastname: Viladomat
authors.forename: Rahul
authors.initials: R
authors.lastname: Mazumder
authors.forename: Alex
authors.initials: A
authors.lastname: McInturff
authors.forename: Douglas J
authors.initials: DJ
authors.lastname: McCauley
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
mesh_terms: D000465:Algorithms; D044822:Biodiversity; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D017753:Ecosystem; D062305:Geographic Mapping; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D018401:Sample Size; D018709:Statistics, Nonparametric
keywords: Geostatistics; Monte-Carlo methods; Resampling; Spatial autocorrelation; Spatial statistics; Variogram