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PreprintarXiv licencearXiv

Statistical Depth for Interval Data: A Closeness-Based Approach and Its Applications

  • Xiaozheng Chen
  • Wenlin Dai
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Abstract

With the rapid development of technology, interval data have been increasingly widely used in various fields, as they can effectively carry information on measurement uncertainty and original variability. Reasonable ranking and outlier screening of interval data are crucial for exploring their value, and statistical depth theory is an important tool to achieve this goal. Existing statistical depth methods are mostly defined for ordinary point-valued data, which are difficult to adapt to the characteristics of interval data and cannot meet the needs of their ranking and outlier detection. Therefore, this paper proposes a statistical depth suitable for interval data and conducts relevant application verification.

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Cite

BibTeXRIS
BibTeX
@misc{chen2026statistical,
  title = {{Statistical Depth for Interval Data: A Closeness-Based Approach and Its Applications}},
  author = {Xiaozheng Chen and Wenlin Dai},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.24641},
  eprint = {2609.24641},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.24641},
}
RIS
TY  - UNPB
TI  - Statistical Depth for Interval Data: A Closeness-Based Approach and Its Applications
AU  - Xiaozheng Chen
AU  - Wenlin Dai
PY  - 2026
DA  - 2026/09/21
PB  - arXiv
DO  - 10.48550/arxiv.2609.24641
UR  - https://arxiv.org/abs/2609.24641
AB  - With the rapid development of technology, interval data have been increasingly widely used in various fields, as they can effectively carry information on measurement uncertainty and original variability. Reasonable ranking and outlier screening of interval data are crucial for exploring their value, and statistical depth theory is an important tool to achieve this goal. Existing statistical depth methods are mostly defined for ordinary point-valued data, which are difficult to adapt to the characteristics of interval data and cannot meet the needs of their ranking and outlier detection. Therefore, this paper proposes a statistical depth suitable for interval data and conducts relevant application verification.
ER  -