Abstract
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 language | English |
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Pages (from-to) | 341-355 |
Number of pages | 15 |
Journal | Stochastic Environmental Research and Risk Assessment |
Volume | 32 |
Issue number | 2 |
DOIs | |
State | Published - 1 Feb 2018 |
Keywords
- Geostatistics
- Information criterion
- Prediction variance
- Resampling
- Squared prediction error