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Activity Number: 198 - SPEED: Nonparametric Statistics: Estimation, Testing, and Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #332881
Title: A DCp Criterion for Nonparametric First Derivative Estimation
Author(s): Sisheng Liu* and Richard Charnigo and Cidambi Srinivasan
Companies: Fred Hutchinson Cancer Research Center and University of Kentucky and University of Kentucky
Keywords: Nonparametric derivative estimation; tuning parameter selection; random design points

Nonparametric derivative estimation depends on tuning parameter selection. For example, the performance of kernel regression and local polynomial regression will depend on the bandwidth h. The spline smooth regression depends on the penalty parameter \lambda. We propose a DCp criterion for general nonparametric first derivative estimation. Our motivation comes from a more general setting with random designed points and non-constant error variance since the derivative estimation with random covariates are common and pivotal in Econometrics. We justify Dcp criterion both theoretically and by simulation. Moreover, we illustrate a potential application of DCp criterion by an example in Economics.

Authors who are presenting talks have a * after their name.

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