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

By Soumendra Nath Lahiri, Jun Zhu. 2006

74 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/dee3e30f329dcb6313b3ad172e4c7c47c4d1d56e

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Citations in the corpus

The Dependent Wild Bootstrap (2010, methodology): “The block-based bootstrap methods have been extended to spatial settings for both regular lattice data and irregular nonlattice observations (see, e.g., Politis and Romano 1993; Politis, Paparoditis, and Romano 1999; Lahiri and Zhu 2006; Zhu and Lahiri 2006).”
Kernel regression estimation for continuous spatial processes (2007, background): “Lahiri and Zhu [29] studied the properties of the M-estimator of a class of spatial regression models under a stochastic sampling design.”