Assessing Parameter Identifiability in Phylogenetic Models Using Data Cloning

• Activity Number: 471
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): José Miguel Ponciano*+
• Companies: University of Florida
• Keywords: Bayesian estimation in Phylogenetics ; Data Cloning ; Maximum Likelihood ; Parameter identifiability ; Parameter estimability ; Diagnostics

Abstract

The success of model-based methods in phylogenetics has motivated much research aimed at generating new, biologically informative models. This new computer-intensive approach to phylogenetics demands validation studies and sound measures of performance. To date there has been little practical guidance available as to when and why the parameters in a particular model can be identi?ed reliably. Here, we illustrate how Data Cloning (DC) can be used to diagnose structural parameter nonidenti?ability (NI) and distinguish it from other parameter estimability problems, including when parameters are structurally identi?able, but are not estimable in a given data set (INE), and when parameters are identi?able, and estimable, but only weakly so (WE). With DC, practitioners and theoreticians can use Bayesian phylogenetics software to diagnose nonidenti?ability and influence of priors in phylogenetics problems. Finally, when applied to phylogenetic inference, DC can be used to study at least two important statistical questions: assessing identi?ability of discrete parameters, like tree topologies, and developing ef?cient sampling methods for computationally expensive posterior densities.