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A regularized MM estimate for interval-valued regression
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DOI：10.1016/j.eswa.2023.122044.png)
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In real life, we usually encounter with interval-valued data when analyzing imprecise data or massive data sets. In this paper, a regularized interval MM estimate (RIMME) for interval-valued regression is proposed. In order to mitigate the mathematical incoherence of the predicted intervals, a regularized term is introduced to penalize the number of crossing intervals. Therefore, the proposed method can achieve a good balance between the prediction accuracy and mathematical coherence of the predicted intervals. To evaluate the performance of RIMME, a simulation study and three real data sets are examined. Experimental results illustrate that our method outperforms five commonly used methods in almost all cases.
Keyword:
Interval-valued data
Outliers
MM estimate
Regularized parameter
Interval crossing
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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