Robust fitting of mixture regression models

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dc.contributor.author Bai, Xiuqin
dc.contributor.author Yao, Weixin
dc.contributor.author Boyer, John E.
dc.date.accessioned 2012-06-18T17:44:48Z
dc.date.available 2012-06-18T17:44:48Z
dc.date.issued 2012-06-18
dc.identifier.uri http://hdl.handle.net/2097/13940
dc.description.abstract The existing methods for tting mixture regression models assume a normal dis- tribution for error and then estimate the regression parameters by the maximum likelihood estimate (MLE). In this article, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a robust estimation procedure and an EM-type algorithm to estimate the mixture regression models. Using a Monte Carlo simulation study, we demon-strate that the proposed new estimation method is robust and works much better than the MLE when there are outliers or the error distribution has heavy tails. In addition, the proposed robust method works comparably to the MLE when there are no outliers and the error is normal. A real data application is used to illustrate the success of the proposed robust estimation procedure. en_US
dc.relation.uri http://www.sciencedirect.com/science/journal/01679473/56/7 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.title Robust fitting of mixture regression models en_US
dc.type Article (author version) en_US
dc.date.published 2012 en_US
dc.citation.doi doi:10.1016/j.csda.2012.01.016 en_US
dc.citation.epage 2359 en_US
dc.citation.issue 7 en_US
dc.citation.jtitle Computational Statistics and Data Analysis en_US
dc.citation.spage 2347 en_US
dc.citation.volume 56 en_US
dc.contributor.authoreid wxyao en_US
dc.contributor.authoreid jboyer en_US
dc.contributor.authoreid xbai en_US

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