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

dc.contributor.authorMeng, Li
dc.date.accessioned2014-06-17T13:18:26Z
dc.date.available2014-06-17T13:18:26Z
dc.date.graduationmonthAugust
dc.date.issued2014-06-17
dc.date.published2014
dc.description.abstractIn 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.
dc.description.advisorWeixin Yao
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Statistics
dc.description.levelMasters
dc.identifier.urihttp://hdl.handle.net/2097/17856
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMixture of linear regression models
dc.subjectModel selection
dc.subjectRobustness
dc.subjectTrimmed likelihood estimator
dc.subject.umiStatistics (0463)
dc.titleRobust estimation of the number of components for mixtures of linear regression
dc.typeReport

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