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Nonparametric estimation of the variance of sample means based on nonstationary spatial data
Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umeå.
2002 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 31, no 10, 1743-1775 p.Article in journal (Refereed) Published
Abstract [en]

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 , where is a finite subset of the integer lattice , 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.

Place, publisher, year, edition, pages
Marcel Dekker, 2002. Vol. 31, no 10, 1743-1775 p.
Keyword [en]
resampling, spatially indexed data, estimation of variance
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-81163DOI: 10.1081/STA-120014912OAI: oai:DiVA.org:umu-81163DiVA: diva2:653039
Available from: 2013-10-02 Created: 2013-10-02 Last updated: 2017-12-06Bibliographically approved

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Output format
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  • asciidoc
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