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Auxiliary Information Assisted MAD Methods for Outlier Detection
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AI
DOI：10.1007/s11424-026-6067-x.png)
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Abstract
En 中文
In practice, data often contain outliers, which can significantly distort the results of traditional statistical methods. Meanwhile, in some practical problems, the proposed objective is to precisely identify outliers. Therefore, it is necessary to perform outlier detection before or in data analysis. The use of auxiliary information generally improves the performance of statistical methods. Building on this idea, a ratio estimator for the Median Absolute Deviation (MAD) is constructed, and its consistency is proven. Based on this estimator, the authors develop novel outlier detection methods that incorporate auxiliary variables into the MAD framework. Simulation results demonstrate that the proposed method outperforms some commonly used outlier detection techniques. An application to the “Body and Brain Weight” dataset also shows the merit of the proposed method.
Keywords:
Auxiliary variable
MAD method
median
outlier detection
ratio estimation
Journal
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2.8
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212
Citations:
2.1K