http://www.nitrc.org/forum/forum.php?forum_id=3623 <p class="abstract"><div class="Abstract" lang="en"><a name="Abs1"></a><span class="AbstractHeading">Abstract&nbsp;&nbsp;</span><div class="normal">We present a least trimmed square (LTS) robust regression method to combine different runs/subjects for second/high level effective connectivity analysis. The basic idea of this method is to treat the extreme nonlinear model variability as outliers if they exceed a certain threshold. A bootstrap method for the LTS estimation is employed to detect model outliers. We compared the LTS robust method with a non-robust method using simulated and real datasets. The difference between LTS and the non-robust method for second level effective connectivity analysis is significant, suggesting the conventional non-robust method is easily affected by the model variability from the first level analysis. In addition, after these outliers are detected and excluded for the high level analysis, the model coefficients of the second level are combined within the framework of a mixed model. The variance of the mixed model is estimated using the Newton–Raphson (NR) type Levenberg-Marquardt algorithm. Three sets of real data are adopted to compare conventional methods which do not include random effects in the analysis with a mixed model for second level effective connectivity analysis. The results show that the conventional method is significantly different from the mixed model when greater model variability exists, suggesting there is a strong random effect, and the mixed model should be employed for the second level effective connectivity analysis. </div> </div></p><ul> <li><span class="labelName">Content Type </span><span class="labelValue">Journal Article</span></li><li>Category Original Article</li><li>Pages 1-14</li><li>DOI 10.1007/s12021-012-9168-8</li><li><span class="labelName">Authors</span><ul> <li>Xingfeng Li, Intelligent Systems Research Centre, University of Ulster at Magee, Derry, UK</li><li>Damien Coyle, Intelligent Systems Research Centre, University of Ulster at Magee, Derry, UK</li><li>Liam Maguire, Intelligent Systems Research Centre, University of Ulster at Magee, Derry, UK</li><li>Thomas Martin McGinnity, Intelligent Systems Research Centre, University of Ulster at Magee, Derry, UK</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li><span class="header labelName">Journal </span><span class="labelValue"><a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></span></li><li><span class="labelName">Online ISSN </span><span class="labelValue">1559-0089</span></li><li><span class="labelName">Print ISSN </span><span class="labelValue">1539-2791</span></li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Sun, 19 Apr 2026 22:08:11 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator