Premium
Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach
Author(s) -
Viladomat Júlia,
Mazumder Rahul,
McInturff Alex,
McCauley Douglas J.,
Hastie Trevor
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12139
Subject(s) - autocorrelation , independence (probability theory) , null hypothesis , nonparametric statistics , spatial analysis , smoothing , monte carlo method , statistics
Summary 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.