Speaker: Sarah Alver

Title: Measurement Error Modeling Applied to Species Tree Inference/Parametric Bootstrap Approach to Multi-factor ANOVA Models w/Unequal Variance & Unbalanced Data 

Abstract:

This talk includes two main topics. The first uses measurement error modeling to improve upon an existing method of inferring species trees from gene trees that were estimated possibly with error. The second involves extending the parametric bootstrap (PB) approach, which was previously shown to work well for one-and two-way analysis of variance models with unequal variance and unbalanced data (heteANOVA), to multi-factor heteANOVA models. An overall framework using PB is presented. For each topic, the underlying theory is shown, and simulations and applications to empirical data are presented, demonstrating improvement over earlier methods. The proposed species tree inference method shows that species tree inference can be improved in the presence of gene tree estimation error, and the new method may be useful for inferring starting trees for other possibly slower methods. The PB methods developed here provide a viable alternative to transforming data to meet the equal variance assumption.