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Serge Guillas, UCLondon Print
Friday, 04 December 2009, 14:30 - 15:30

Bivariate Splines for Spatial Functional Regression Models

Serge Guillas, Department of Statistical Science, University College London

Abstract:  We consider the functional linear regression model where the explanatory variable is a random surface and the response is a real random variable, in various situations where both the explanatory variable and the noise can be unbounded an dependent. Bivariate splines over triangulations represent the random surfaces. We use this representation to construct least squares estimators of the regression function with a penalization term. Under the assumptions that the regressors in the sample span a large enough space of functions, bivariate splines approximation properties yield the consistency of the estimators. Simulations demonstrate the quality of the asymptotic properties on a realistic domain. We also carry out an application to ozone concentration forecasting over the US that illustrates the predictive skills of the method. Finally, we present recent results of long-term seabed forecasting using this technique.

Location: 2NO.906
Contact: Jacqueline Douilly, This e-mail address is being protected from spam bots, you need JavaScript enabled to view it

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