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

S. N. Lahiri, Jun Zhu

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1774-1813
Number of pages40
JournalAnnals of Statistics
Volume34
Issue number4
DOIs
StatePublished - Aug 2006

Keywords

  • Block bootstrap method
  • Increasing domain asymptotics
  • Infill sampling
  • Random field
  • Spatial sampling design
  • Strong mixing

Fingerprint

Dive into the research topics of 'Resampling methods for spatial regression models under a class of stochastic designs'. Together they form a unique fingerprint.

Cite this