ChauBoxplot and AdaptiveBoxplot: Two R packages for boxplot-based outlier detection
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912938190700544 |
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| author | Tong, Tiejun Lin, Hongmei Gang, Bowen Zhang, Riquan |
| author_facet | Tong, Tiejun Lin, Hongmei Gang, Bowen Zhang, Riquan |
| contents | Tukey's boxplot is widely used for outlier detection; however, its classic fixed-fence rule tends to flag an excessive number of outliers as the sample size grows. To address this, we introduce two new R packages, ChauBoxplot and AdaptiveBoxplot, which implement more robust and statistically principled outlier detection methods. We illustrate their advantages and practical implications through comprehensive simulation studies and a real-world analysis of provincial university admission rates from China's National College Entrance Examination. Based on these findings, we provide practical guidance to help practitioners select appropriate boxplot methods, achieving a balance between interpretability and statistical reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13759 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ChauBoxplot and AdaptiveBoxplot: Two R packages for boxplot-based outlier detection Tong, Tiejun Lin, Hongmei Gang, Bowen Zhang, Riquan Methodology Tukey's boxplot is widely used for outlier detection; however, its classic fixed-fence rule tends to flag an excessive number of outliers as the sample size grows. To address this, we introduce two new R packages, ChauBoxplot and AdaptiveBoxplot, which implement more robust and statistically principled outlier detection methods. We illustrate their advantages and practical implications through comprehensive simulation studies and a real-world analysis of provincial university admission rates from China's National College Entrance Examination. Based on these findings, we provide practical guidance to help practitioners select appropriate boxplot methods, achieving a balance between interpretability and statistical reliability. |
| title | ChauBoxplot and AdaptiveBoxplot: Two R packages for boxplot-based outlier detection |
| topic | Methodology |
| url | https://arxiv.org/abs/2601.13759 |