Assessing parameter identifiability in phylogenetic models using data cloning.

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 identified reliably. Here, we illustrate how Data Cloning (DC), a recently developed methodology to compute the maximum likelihood estimates along with their asymptotic variance, can be used to diagnose structural parameter nonidentifiability (NI) and distinguish it from other parameter estimability problems, including when parameters are structurally identifiable, but are not estimable in a given data set (INE), and whe

SUBMITTER: Ponciano JM 

PROVIDER: S-EPMC3478565 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature