Robust estimation of the number of components for mixtures of linear regression

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dc.contributor.author Meng, Li en_US
dc.date.accessioned 2014-06-17T13:18:26Z
dc.date.available 2014-06-17T13:18:26Z
dc.date.issued 2014-06-17
dc.identifier.uri http://hdl.handle.net/2097/17856
dc.description.abstract In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Mixture of linear regression models en_US
dc.subject Model selection en_US
dc.subject Robustness en_US
dc.subject Trimmed likelihood estimator en_US
dc.title Robust estimation of the number of components for mixtures of linear regression 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 2014 en_US
dc.date.graduationmonth August en_US


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