Smoothed nonparametric derivative estimation using weighted difference quotients

被引：0
|
作者：
Liu, Yu [1 ]
de Brabanter, Kris [2 ]
机构：
[1] Department of Computer Science, Iowa State University, Ames,IA,50011, United States
[2] Department of Statistics, Department of Industrial Manufacturing and Systems Engineering, Iowa State University, Ames,IA,50011, United States
来源：
Journal of Machine Learning Research | 2020年 / 21卷
关键词：
Derivatives play an important role in bandwidth selection methods (e.g; plug-ins); data analysis and bias-corrected confidence intervals. Therefore; obtaining accurate derivative information is crucial. Although many derivative estimation methods exist; the majority require a fixed design assumption. In this paper; we propose an effective and fully data-driven framework to estimate the first and second order derivative in random design. We establish the asymptotic properties of the proposed derivative estimator; and also propose a fast selection method for the tuning parameters. The performance and flexibility of the method is illustrated via an extensive simulation study. ©2020 Yu Liu & Kris De Brabanter;
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