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Activity Number: 161 - SPEED: Nonparametrics and Imaging
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #324832 View Presentation
Title: Weighted Quantile Regression Splines using Total Variation Regularization
Author(s): Jae-Hwan Jhong* and Ja-Yong Koo
Companies: Korea University and Korea University
Keywords: B-spline ; coordinate descent algorithm ; bisection ; total variation ; weighted quantile

We carry out a study on penalized regression spline estimator with total variation penalty. Once we express the estimator by a linear combination of B-splines, the coefficients are estimated by minimizing a penalized check loss function. A coordinate descent algorithm is introduced to handle the check loss term and the total variation penalty determined by the B-spline coefficients. In implementation, we adopt the weighted quantile and bisection method to find the minimum of each coordinate. We also illustrate the performance of the proposed method using some numerical studies.

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

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