Local polynomial regression for circular predictors

Abstract

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.