Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37086
標題: Robust mixture modeling using the skew t distribution
作者: Lin, T.I.
林宗儀
Lee, J.C.
Hsieh, W.J.
關鍵字: EM-type algorithms
maximum likelihood
outlying observations
PX-EM
algorithm
skew t mixtures
truncated normal
maximum-likelihood
bayesian-analysis
unknown number
em algorithm
multivariate
components
extension
ecm
期刊/報告no:: Statistics and Computing, Volume 17, Issue 2, Page(s) 81-92.
摘要: 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.
URI: http://hdl.handle.net/11455/37086
ISSN: 0960-3174
文章連結: http://dx.doi.org/10.1007/s11222-006-9005-8
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