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Contaminated Kent mixture model for clustering non-spherical directional data with heavy tails or scatter

Author

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  • Dong, Aqi
  • Melnykov, Volodymyr

Abstract

To cluster asymmetrically distributed data on a sphere, a Kent mixture model is commonly used. However, the performance of such a model can be severely affected by the presence of heavy tails or outliers. A novel contaminated Kent mixture model is proposed to alleviate this issue. As demonstrated via a series of simulation studies and applications to real-life data sets, the developed model shows superior performance over existing alternatives for non-spherical heavy-tailed data.

Suggested Citation

  • Dong, Aqi & Melnykov, Volodymyr, 2024. "Contaminated Kent mixture model for clustering non-spherical directional data with heavy tails or scatter," Statistics & Probability Letters, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:stapro:v:208:y:2024:i:c:s0167715224000270
    DOI: 10.1016/j.spl.2024.110058
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