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.

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