Robust mixture regression model fitting by Laplace distribution
dc.contributor.author | Xing, Yanru | |
dc.date.accessioned | 2013-09-27T19:06:21Z | |
dc.date.available | 2013-09-27T19:06:21Z | |
dc.date.graduationmonth | December | en_US |
dc.date.issued | 2013-09-27 | |
dc.date.published | 2013 | en_US |
dc.description.abstract | A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method. | en_US |
dc.description.advisor | Weixing Song | en_US |
dc.description.degree | Master of Science | en_US |
dc.description.department | Department of Statistics | en_US |
dc.description.level | Masters | en_US |
dc.identifier.uri | http://hdl.handle.net/2097/16534 | |
dc.language.iso | en_US | en_US |
dc.publisher | Kansas State University | en |
dc.subject | EM algorithm | en_US |
dc.subject | Laplace distribution | en_US |
dc.subject | Least absolute deviation | en_US |
dc.subject | Mixture regression model | en_US |
dc.subject.umi | Statistics (0463) | en_US |
dc.title | Robust mixture regression model fitting by Laplace distribution | en_US |
dc.type | Report | en_US |