Robust mixture linear EIV regression models by t-distribution
| dc.contributor.author | Liu, Yantong | |
| dc.date.accessioned | 2012-12-10T14:30:52Z | |
| dc.date.available | 2012-12-10T14:30:52Z | |
| dc.date.graduationmonth | December | en_US |
| dc.date.issued | 2012-12-10 | |
| dc.date.published | 2013 | en_US |
| dc.description.abstract | A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the t-distribution is a scale mixture of normal distribution and a Gamma distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies. Comparison is also made with the MLE procedure under normality assumption. | 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/2097/15157 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Kansas State University | en |
| dc.subject | Robust estimation | en_US |
| dc.subject | Linear errors-in-variables model | en_US |
| dc.subject | EM algorithm | en_US |
| dc.subject | Mixture | en_US |
| dc.subject | T-distribution | en_US |
| dc.subject.umi | Statistics (0463) | en_US |
| dc.title | Robust mixture linear EIV regression models by t-distribution | en_US |
| dc.type | Report | en_US |
