Abstract

Subsampling and block resampling methods have been suggested in the literature to nonparametrically estimate the variance of statistics computed from spatial data. Usually stationary data are required. However, in empirical applications, the assumption of stationarity often must be rejected. This article proposes nonparametric methods to estimate the variance of (functions of) sample means based on nonstationary spatial data using subsampling. We assume that data are observed on a lattice in some region of R-2. In the data that we consider, the information in the different picture elements (pixels) of the lattice are allowed to come from different distributions, with smoothly varying expected values, or with expected values decomposed additively into directional components. Furthermore, pixels are assumed to be locally dependent, and the dependence structure is allowed to differ over the lattice. Consistent variance estimators for (functions of) sample means, together with convergence rates in mean square, are provided under these assumptions. An example with applications to forestry, using satellite data, is discussed

Keywords

bootstrap; nonidentically distributed variables; nonindependent variables; resampling

Published in

Journal of the American Statistical Association
2004, Volume: 99, number: 465, pages: 82-95
Publisher: AMER STATISTICAL ASSOC

SLU Authors

• Ekström, Magnus

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.1198/016214504000000106

Permanent link to this page (URI)

https://res.slu.se/id/publ/4986