DocumentCode :
621788
Title :
A modified partial robust M-regression to improve prediction performance for data with outliers
Author :
Yin, Shen ; Wang, Guang
Author_Institution :
Harbin Institute of Technology, Harbin, 150001, China
fYear :
2013
fDate :
28-31 May 2013
Firstpage :
1
Lastpage :
6
Abstract :
This paper introduces a modified partial robust M-regression approach. The objective of the new approach is to improve the prediction accuracy of the regression model for data containing outliers. The original PRM is an efficient robust linear regression method which is devoting to down-weighting the outliers by choosing proper weighting scheme with relatively less computational load. Although PRM shows superior performance compared to the existing approaches, it fails to make all the residual weights for outlier coverage to zeros within the iteration steps, which indicates the calculated regression model may be still affected by these outliers. Based on a novel distance measurement method and a corresponding center estimate method, a modified partial robust M-regression approach called mPRM is presented to overcome the drawback of PRM. Simulation study shows that the new approach not only inherits the robustness and efficiency of PRM, but also has a more accurate prediction performance than PRM.
Keywords :
Accuracy; Algorithm design and analysis; Convergence; Euclidean distance; Prediction algorithms; Predictive models; Robustness; PRM; prediction accuracy; tSL-center; tSL-distance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics (ISIE), 2013 IEEE International Symposium on
Conference_Location :
Taipei, Taiwan
ISSN :
2163-5137
Print_ISBN :
978-1-4673-5194-2
Type :
conf
DOI :
10.1109/ISIE.2013.6563843
Filename :
6563843
Link To Document :
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