Back to results list
Please use this identifier to cite or link to this item:
|Title:||Comparative Analysis for Robust Penalized Spline Smoothing Methods||Authors:||Wang, B
|Issue Date:||2014||Publisher:||Hindawi Publishing Corporation||Source:||Mathematical problems in engineering, 2014, v. 2014, 642475 How to cite?||Journal:||Mathematical problems in engineering||Abstract:||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.||URI:||http://hdl.handle.net/10397/28907||ISSN:||1024-123X||EISSN:||1563-5147||DOI:||10.1155/2014/642475||Rights:||Copyright © 2014 Bin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Wang, B., Shi, W., & Miao, Z. (2014). Comparative analysis for robust penalized spline smoothing methods. Mathematical Problems in Engineering, 2014, is available at https//doi.org/10.1155/2014/642475
|Appears in Collections:||Journal/Magazine Article|
Show full item record
Files in This Item:
|Wang_Comparative_Analysis_Robust.pdf||2.86 MB||Adobe PDF||View/Open|
Citations as of Jun 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.