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August 2006 Resampling methods for spatial regression models under a class of stochastic designs
S. N. Lahiri, Jun Zhu
Ann. Statist. 34(4): 1774-1813 (August 2006). DOI: 10.1214/009053606000000551


In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block bootstrap method provides a valid approximation to the distribution of a class of M-estimators of the spatial regression parameters. Finite sample properties of the method are investigated through a moderately large simulation study and a real data example is given to illustrate the methodology.


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S. N. Lahiri. Jun Zhu. "Resampling methods for spatial regression models under a class of stochastic designs." Ann. Statist. 34 (4) 1774 - 1813, August 2006.


Published: August 2006
First available in Project Euclid: 3 November 2006

zbMATH: 1246.62117
MathSciNet: MR2283717
Digital Object Identifier: 10.1214/009053606000000551

Primary: 62G09
Secondary: 62M30

Keywords: Block bootstrap method , increasing domain asymptotics , infill sampling , Random field , spatial sampling design , Strong mixing

Rights: Copyright © 2006 Institute of Mathematical Statistics


Vol.34 • No. 4 • August 2006
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