Derivative Estimation in Random Design

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Yu Liu, Kris De Brabanter


We propose a nonparametric derivative estimation method for random design without having to estimate the regression function. The method is based on a variance-reducing linear combination of symmetric difference quotients. First, we discuss the special case of uniform random design and establish the estimator’s asymptotic properties. Secondly, we generalize these results for any distribution of the dependent variable and compare the proposed estimator with popular estimators for derivative estimation such as local polynomial regression and smoothing splines.