Abstract
A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.
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Lin, T.I., Lee, J.C. & Hsieh, W.J. Robust mixture modeling using the skew t distribution. Stat Comput 17, 81–92 (2007). https://doi.org/10.1007/s11222-006-9005-8
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DOI: https://doi.org/10.1007/s11222-006-9005-8