Statistics and Computing

, Volume 17, Issue 2, pp 81–92 | Cite as

Robust mixture modeling using the skew t distribution

  • Tsung I. LinEmail author
  • Jack C. Lee
  • Wan J. Hsieh


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.


EM-type algorithms Maximum likelihood Outlying observations PX-EM algorithm Skew t mixtures Truncated normal 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Applied-MathematicsNational Chung Hsing UniversityTaichungTaiwan
  2. 2.Graduate Institute of FinanceNational Chiao Tung UniversityHsinchuTaiwan
  3. 3.Institute of StatisticsNational Chiao Tung UniversityHsinchuTaiwan

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