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Activity Number:
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112
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #318945
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Title:
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Mixtures of Regression Models for Large Spatial Data Sets
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Author(s):
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Amanda Hering* and Karen Kazor and Laura Condon and Reed Maxwell
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Companies:
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Colorado School of Mines and Colorado School of Mines and Colorado School of Mines and Colorado School of Mines
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Keywords:
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Mixture of regression model ;
Spatial trends ;
Classification
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Abstract:
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When a spatial regression model that links a response variable to a set of explanatory variables is desired, it is unlikely that the same regression model holds throughout the domain when the spatial dataset is very large and complex. The locations where the trend changes may not be known, and we present here a mixture of regression models approach to identifying the locations wherein the relationship between the predictors and the response is similar; to estimating the model within each group; and to estimating the number of groups. An EM algorithm for estimating these models is presented along with a criteria for choosing the number of groups. An example with groundwater depth and associated predictors generated from a large physical model simulation demonstrates the fit and interpretation of the proposed models.
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Authors who are presenting talks have a * after their name.
