A composite spatial predictor via local criteria under a misspecified model

作者：Chen Chun Shu; Chen Chao Sheng^*

来源：Stochastic Environmental Research and Risk Assessment, 2018, 32(2): 341-355.

DOI：10.1007/s00477-017-1438-4

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

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.

• 出版日期2018-2