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National Chung Hsing University Institutional Repository - NCHUIR > 理學院 > 統計學研究所 > 依資料類型分類 > 期刊論文 >  Computationally efficient learning of multivariate t mixture models with missing information

Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/133586

標題: Computationally efficient learning of multivariate t mixture models with missing information
作者: Lin, T.I.;Ho, H.J.;Shen, P.S.
林宗儀
關鍵字: Classifier;Learning with missing information;Multivariate t mixture;models;PX-EM algorithm;Outlying observations;em algorithm;maximum-likelihood;bayesian-analysis;cluster-analysis;unknown number;distributions;robust;inference;ecm;components
日期: 2009
Issue Date: 2012-12-14 10:05:43 (UTC+8)
關連: Computational Statistics, Volume 24, Issue 3, Page(s) 375-392.
摘要: 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 flexible mixture analyzer that allows practitioners to handle real-world multivariate data sets with complex missing patterns in a more efficient manner. 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 synthetic missing values.
Relation: Computational Statistics
Appears in Collections:[依資料類型分類] 期刊論文
[依教師分類] 林宗儀
[依教師分類] 林宗儀

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