Local Spatial-Predictor Selection. Bradley, J., Cressie, N., & Shi, T. In volume 2013, of Centre for Statistical & Survey Methodology Working Paper Series, pages 09-13+. ![Local Spatial-Predictor Selection [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Consider the problem of spatial prediction of a random process from a spatial dataset. Global spatial-predictor selection provides a way to choose a single spatial predictor from a number of competing predictors. Instead, we consider local spatial-predictor selection at each spatial location in the domain of interest. This results in a hybrid predictor that could be considered global, since it takes the form of a combination of local predictors; we call this the locally selected spatial predictor. We pursue this idea here using the (empirical) deviance information as our criterion for (global and local) predictor selection. In a small simulation study, the relative performance of this combined predictor, relative to the individual predictors, is assessed.
@incollection{bradleyLocalSpatialpredictorSelection2013,
title = {Local Spatial-Predictor Selection},
author = {Bradley, Jonathan and Cressie, Noel and Shi, Tao},
date = {2013},
volume = {2013},
pages = {09-13+},
url = {http://mfkp.org/INRMM/article/14271231},
abstract = {Consider the problem of spatial prediction of a random process from a spatial dataset. Global spatial-predictor selection provides a way to choose a single spatial predictor from a number of competing predictors. Instead, we consider local spatial-predictor selection at each spatial location in the domain of interest. This results in a hybrid predictor that could be considered global, since it takes the form of a combination of local predictors; we call this the locally selected spatial predictor. We pursue this idea here using the (empirical) deviance information as our criterion for (global and local) predictor selection. In a small simulation study, the relative performance of this combined predictor, relative to the individual predictors, is assessed.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14271231,adaptive-selection,local-scale,predictor-selection,predictors,rank-based-analysis,weighting},
series = {Centre for {{Statistical}} \& {{Survey Methodology Working Paper Series}}}
}
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