Robust mixture regression using the t-distribution

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dc.contributor.author Yao, Weixin
dc.contributor.author Wei, Yan
dc.contributor.author Yu, Chun
dc.date.accessioned 2014-03-10T19:51:33Z
dc.date.available 2014-03-10T19:51:33Z
dc.date.issued 2014-03-10
dc.identifier.uri http://hdl.handle.net/2097/17209
dc.description.abstract The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the t-distribution by extending the mixture of t-distributions to the regression setting is proposed. However, this proposed new mixture regression model is still not robust to high leverage outliers. In order to overcome this, a modified version of the proposed method, which fits the mixture regression based on the t-distribution to the data after adaptively trimming high leverage points, is also proposed. Furthermore, it is proposed to adaptively choose the degrees of freedom for the t-distribution using profile likelihood. The proposed robust mixture regression estimate has high efficiency due to the adaptive choice of degrees of freedom. en_US
dc.language.iso en_US en_US
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S0167947313002648 en_US
dc.subject EM algorithm en_US
dc.subject Mixture regression models en_US
dc.subject Outliers en_US
dc.subject Robust regression en_US
dc.subject t-distribution en_US
dc.title Robust mixture regression using the t-distribution en_US
dc.type Article (author version) en_US
dc.date.published 2014 en_US
dc.citation.doi doi:10.1016/j.csda.2013.07.019 en_US
dc.citation.epage 127 en_US
dc.citation.jtitle Computational Statistics and Data Analysis en_US
dc.citation.spage 116 en_US
dc.citation.volume 71 en_US
dc.contributor.authoreid wxyao en_US


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