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Interval joint robust regression method
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AI
DOI：10.1016/j.neucom.2021.08.129.png)
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摘要
En 中文
Interval-valued data are needed to manage either the uncertainty related to measurements, or the vari-ability inherent to the description of complex objects representing group of individuals. A number of regression methods suitable to interval variables describing variability of complex objects are already available. However, less attention has been given to methods that, simultaneously, take into account the full interval information and are resistant to interval outlier observations, even with the frequent presence of atypical observations on interval-valued data sets. This paper proposes a new robust linear regression method for interval variables, where the presence of outliers either in the center or in the radius penalize both the center and the radius regression models. Moreover, the interval observations with outliers on both center and radius are more penalized than those observations with outliers only in the center (or in the radius). Besides, this paper provides a suitable iterative algorithm to estimate the parameters of the proposed method. The algorithm estimates the parameters of the center (or of the radius) model taking into account both information of the center and the radius. The convergence and time complexity of the iterative algorithm are also presented. Finally, the performance of the new method is compared with some previous robust regression approaches and evaluated on synthetic and real interval-valued data sets. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Interval-valued variables
Exponential-type kernel functions
Robust regression models
Width hyper-parameter estimators
Outliers
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W