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Comparative Analysis for Robust Penalized Spline Smoothing Methods
Joint Authors
Shi, Wenzhong
Miao, Zelang
Wang, Bin
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Smoothing noisy data is commonly encountered in engineering domain, and currently robust penalized regression spline models are perceived to be the most promising methods for coping with this issue, due to their flexibilities in capturing the nonlinear trends in the data and effectively alleviating the disturbance from the outliers.
Against such a background, this paper conducts a thoroughly comparative analysis of two popular robust smoothing techniques, the M-type estimator and S-estimation for penalized regression splines, both of which are reelaborated starting from their origins, with their derivation process reformulated and the corresponding algorithms reorganized under a unified framework.
Performances of these two estimators are thoroughly evaluated from the aspects of fitting accuracy, robustness, and execution time upon the MATLAB platform.
Elaborately comparative experiments demonstrate that robust penalized spline smoothing methods possess the capability of resistance to the noise effect compared with the nonrobust penalized LS spline regression method.
Furthermore, the M-estimator exerts stable performance only for the observations with moderate perturbation error, whereas the S-estimator behaves fairly well even for heavily contaminated observations, but consuming more execution time.
These findings can be served as guidance to the selection of appropriate approach for smoothing the noisy data.
American Psychological Association (APA)
Wang, Bin& Shi, Wenzhong& Miao, Zelang. 2014. Comparative Analysis for Robust Penalized Spline Smoothing Methods. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-487596
Modern Language Association (MLA)
Wang, Bin…[et al.]. Comparative Analysis for Robust Penalized Spline Smoothing Methods. Mathematical Problems in Engineering No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-487596
American Medical Association (AMA)
Wang, Bin& Shi, Wenzhong& Miao, Zelang. Comparative Analysis for Robust Penalized Spline Smoothing Methods. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-487596
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487596