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

Resampling methods for spatial regression models under a class of stochastic designs