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Abstract Details
Activity Number:
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137
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 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 - #306524 |
Title:
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Spatial Functional Regression for Misaligned Data
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Author(s):
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Catherine Tuglus*+ and Meredith Franklin
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Companies:
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University of Southern California and University of Southern California
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Address:
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2001 N. Soto St, Los Angeles, CA, 90089, United States
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Keywords:
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functional data analysis ;
spatial statistics ;
spatial misalignment ;
air pollution ;
remote sensing
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Abstract:
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We have developed novel spatial functional regression approach for predicting a response surface based on data from different spatial supports. The model incorporates bivariate functional predictors belonging to a 2-d functional space that are weighted by a non-parametric function of distance between the locations of the response and predictor variables. The method allows us to make predictions based on response and predictor variables that are not necessarily measured at the same location but are separated by a fixed distance. We compare our method to standard approaches in simulation. Predicted values from the functional approach are shown to have substantially less uncertainty than those generated from techniques such as universal kriging. The method is applied to model air pollution concentrations (nitrogen dioxide, NO2) measured at monitoring stations in Southern California as a function of satellite observations of tropospheric NO2 and meteorological data from weather stations.
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