Robust mixture regression modeling with Pearson type VII distribution

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dc.contributor.author Zhang, Jingyi
dc.date.accessioned 2013-04-26T18:46:39Z
dc.date.available 2013-04-26T18:46:39Z
dc.date.issued 2013-04-26
dc.identifier.uri http://hdl.handle.net/2097/15648
dc.description.abstract A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the Pearson type VII distributions are a scale mixture of a normal distribution and a Gamma distribution. A trimmed version of proposed procedure is also discussed in this paper, which can successfully trim the high leverage points away from the data. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in the literature. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Mixture model en_US
dc.subject Pearson type VII distribution en_US
dc.subject EM algorithm en_US
dc.subject Robust regreesion en_US
dc.title Robust mixture regression modeling with Pearson type VII 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 Weixing Song en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2013 en_US
dc.date.graduationmonth May en_US


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