  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.
