Automation of likely outliers detection in linear mixed models
| Author(s) | Wang, Yue | |
| Author(s) | ||
| Date Accessioned | 2014-06-16T13:49:31Z | |
| Date Available | 2014-06-16T13:49:31Z | |
| Publication Date | 2013 | |
| Abstract | It is difficult to detect outliers in linear mixed models. The traditional way of identifying outliers is to check whether there are any violations in model assumptions by examining the normal QQ plot and the residual plot. A simulation approach proposed by Schützenmeister and Piepho adds the objectivity in interpreting results of the QQ and residual plot. Based on this simulation approach, a software tool is developed to indentify potential outliers in linear mixed models automatically. In addition, the performance of this approach is evaluated. This tool is user-friendly to inexperienced analysts and open sourced. | en_US |
| Advisor | Lee, Jong Soo, | |
| Advisor | Wisser, Randall J. | |
| Degree | M.S. | |
| Department | University of Delaware, Department of Statistics | |
| URL | http://udspace.udel.edu/handle/19716/13049 | |
| Publisher | University of Delaware | en_US |
| dc.subject.lcsh | Outliers (Statistics) | |
| dc.subject.lcsh | Linear models (Statistics) | |
| dc.subject.lcsh | Computer software. | |
| Title | Automation of likely outliers detection in linear mixed models | en_US |
| Type | Thesis | en_US |