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A0161
Title: Modeling space-time functional data with complex dependencies via regression with partial differential regularizations Authors:  Mara Sabina Bernardi - Politecnico di Milano (Italy) [presenting]
Laura Sangalli - Politecnico di Milano (Italy)
Gabriele Mazza - Politecnico di Milano (Italy)
James Ramsay - McGill University (Canada)
Abstract: A semiparametric model for the analysis of functional data with complex dependencies is presented. The data considered can be seen as spatially dependent curves or time dependent surfaces. The model is based on the idea of regression with partial differential regularizations. Among the various modeling features, the proposed method is able to deal with spatial domains featuring peninsulas, islands and other complex geometries. Space-varying covariate information is included in the model via a semiparametric framework. The estimators have a penalized regression form, they are linear in the observed data values, and have good inferential properties. The use of numerical analysis techniques, and specifically of finite elements, makes the model computationally very efficient. The model is compared via simulations to other spatio-temporal techniques and it is illustrated via an application to the study of the annual production of waste in the municipalities of Venice province.