Comparing two spatial variables with the probability of agreement
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Date
2024
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Abstract
Computing the agree ment betwee n 2 con tinuous sequences is of grea t interest in statistics when comparing 2 instruments or one instrument with a gold standard. The probability of agree ment quantifies the similarity between 2 variables of interest, and it is useful for determining what constitutes a practically important difference. In this article, we introduce a generalization of the PA for the treatment of spatial vari ables. Our proposal makes the PA dependent on the spatial lag. We establish the conditions for which the PA decays as a function of the distance lag for isotropic stationary and nonstationary spatial processes . Estimtion is addr essed through a first-order appr oxima tion that guarantees the asymp totic normality of the sample version of the PA. The sensitivity of the PA with respect to the covariance parame ters is studied for finite sample size. The new method is described and illustrated with real data involving autumnal changes in the green chromatic coordinate ( G cc ) , an index of “greeness ”that captures the phenological stage of tree leaves, is associ ated with carbon flux from econsys tems, and is estimated from repeated images of forest canopies.
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Keywords
Bivariate gaussian spatial process, Covariance functions, G cc index, Spatiotemporal process