Spatial Sampling for Model Selection - eScholarship

Many applications in sensor networks require the estimation of spatial environmental fields. We focus on the applications where the estimation is done by fitting a parametric model to the field. We study the case when the parametric model structure is unknown. Instead of assuming a particular structure, we introduce uncertainty by assuming that the spatial field is one of multiple plausible models. We then set a likelihood test for the model selection and find a spatial sampling strategy that optimizes the test. The strategy finds the locations that result in a minimum probability of error in the selection of the correct model structure. The strategy is introduced by Atkinson and Fedorov and is called T-design. We present as well the benefit over the passive (random strategy) data collection.