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Research article2002Peer reviewedOpen access

Nonparametric estimation of the variance of sample means based on nonstationary spatial data

Ekstrom, M


In Politis and Romano (Politis, D.N.; Romano, J.P. Nonparametric Resampling for Homogeneous Strong Mixing Random Fields. Journal of Multivariate Analysis 1993; 47, 301-328.), different block resampling estimators of variance of general linear statistics, e.g., a sample mean, were proposed under the assumption of stationarity. In the present, paper such estimators of variance of sample means, computed from nonstationary spatially indexed data {X-i : i is an element of A}, where A is a finite subset of the integer lattice Z(2), are studied. Consistency of estimators of variance will be shown for the following kind of data: Observations taken from different lattice points are allowed to come from different distributions, and the dependence structure is allowed to differ over the lattice. We assume that all observed values are from distributions with the same expected value, or with expected values that decompose additively into directional components. Furthermore, it will be assumed that observations separated by a certain distance are independent.


resampling; spatially indexed data; estimation of variance

Published in

Communications in Statistics - Theory and Methods
2002, Volume: 31, number: 10, pages: 17751743

      SLU Authors

    • Ekström, Magnus

      • Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences

    UKÄ Subject classification

    Probability Theory and Statistics

    Publication identifier


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