Robust mixture regression models using t-distribution

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dc.contributor.author Wei, Yan
dc.date.accessioned 2012-08-01T13:40:19Z
dc.date.available 2012-08-01T13:40:19Z
dc.date.issued 2012-08-01
dc.identifier.uri http://hdl.handle.net/2097/14110
dc.description.abstract In this report, we propose a robust mixture of regression based on t-distribution by extending the mixture of t-distributions proposed by Peel and McLachlan (2000) to the regression setting. This new mixture of regression model is robust to outliers in y direction but not robust to the outliers with high leverage points. In order to combat this, we also propose a modified version of the proposed method, which fits the mixture of regression based on t-distribution to the data after adaptively trimming the high leverage points. We further propose to adaptively choose the degree of freedom for the t-distribution using profile likelihood. The proposed robust mixture regression estimate has high efficiency due to the adaptive choice of degree of freedom. We demonstrate the effectiveness of the proposed new method and compare it with some of the existing methods through simulation study. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
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 models using t-distribution en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Weixin Yao en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth August en_US

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