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Derivative Estimation in Random Design
Yu Liu · Kris De Brabanter

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 210 #57

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.

Author Information

Yu Liu (Iowa State University)

PhD candidate in Computer Science, Iowa State University M.S. in Statistics, Iowa State University Research interests: machine learning, nonparametric regression.

Kris De Brabanter (ISU)