A composite spatial predictor via local criteria under a misspecified model

  • Chun-Shu Chen
  • Chao-Sheng Chen
Original Paper


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.


Geostatistics Information criterion Prediction variance Resampling Squared prediction error 



We thank the Editor, an associate editor, and two anonymous referees for their helpful comments and suggestions. This work was supported by the Ministry of Science and Technology of Taiwan under Grant MOST 104-2118-M-018-002-MY2.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute of Statistics and Information ScienceNational Changhua University of EducationChanghuaTaiwan

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