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Mathematical Sciences Colloquium by Amanda Hering
Friday, Apr 21
3 p.m. - 4 p.m. Location: FO 2.702

Amanda Hering

Baylor University

Mixture of Regression Models for Large Spatial Data Sets

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.


Sponsored by the Department of Mathematical Sciences

Contact Info:
Viswanath Ramakrishna, 972-883-6873
Questions? Email me.

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