A composite spatial predictor via local criteria under a misspecified model

Chun Shu Chen, Chao Sheng Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Spatial prediction and variable selection for the study area are both important issues in geostatistics. If spatially varying means exist among different subareas, globally fitting a spatial regression model for observations over the study area may be not suitable. To alleviate deviations from spatial model assumptions, this paper proposes a methodology to locally select variables for each subarea based on a locally empirical conditional Akaike information criterion. In this situation, the global spatial dependence of observations is considered and the local characteristics of each subarea are also identified. It results in a composite spatial predictor which provides a more accurate spatial prediction for the response variables of interest in terms of the mean squared prediction errors. Further, the corresponding prediction variance is also evaluated based on a resampling method. Statistical inferences of the proposed methodology are justified both theoretically and numerically. Finally, an application of a mercury data set for lakes in Maine, USA is analyzed for illustration.

Original languageEnglish
Pages (from-to)341-355
Number of pages15
JournalStochastic Environmental Research and Risk Assessment
Issue number2
StatePublished - 1 Feb 2018


  • Geostatistics
  • Information criterion
  • Prediction variance
  • Resampling
  • Squared prediction error


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