Efficient computational schemes for supervised learning ofmultivariate t mixture models with missing information
碩士 === 東海大學 === 統計學系 === 95 === A finite mixture model using the multivariate t distribution has been well recognized as a robust extension of Gaussian mixtures. This paper presents an efficient PX-EM algorithm for supervised learning of multivariate t mixture models in the presence of missing value...
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| Format: | Others |
| Language: | en_US |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/30114475996940869491 |
| Summary: | 碩士 === 東海大學 === 統計學系 === 95 === A finite mixture model using the multivariate t distribution has been well
recognized as a robust extension of Gaussian mixtures. This paper presents an
efficient PX-EM algorithm for supervised learning of multivariate t mixture
models in the presence of missing values. To simplify the development of new
theoretic results and facilitate the implementation of the PX-EM algorithm,
two auxiliary indicator matrices are incorporated into the model and shown to
be effective. The proposed methodology is a generalized approach for robust
mixture models that allow analysts to handle real-world multivariate data sets
with complex missing patterns. The performance of computational aspects is
investigated through a simulation study and the procedure is also applied to
the analysis of real data with varying proportions of artificially missing values.
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