We consider local smoothing of datasets where the design space is the d-dimensional (d≥1) torus and the response variable is real-valued. Our purpose is to extend least squares local polynomial fitting to this situation. We give both theoretical and empirical results.
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Marzio et al. (2009) studied this question.
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