Dated ancestral trees from binary trait data and their application to the diversification of languages

Geoff K. Nicholls and Russell D. Gray

Journal of the Royal Statistical Society Series B, 2008, vol. 70, issue 3, 545-566

Abstract: Summary. Binary trait data record the presence or absence of distinguishing traits in individuals. We treat the problem of estimating ancestral trees with time depth from binary trait data. Simple analysis of such data is problematic. Each homology class of traits has a unique birth event on the tree, and the birth event of a trait that is visible at the leaves is biased towards the leaves. We propose a model‐based analysis of such data and present a Markov chain Monte Carlo algorithm that can sample from the resulting posterior distribution. Our model is based on using a birth–death process for the evolution of the elements of sets of traits. Our analysis correctly accounts for the removal of singleton traits, which are commonly discarded in real data sets. We illustrate Bayesian inference for two binary trait data sets which arise in historical linguistics. The Bayesian approach allows for the incorporation of information from ancestral languages. The marginal prior distribution of the root time is uniform. We present a thorough analysis of the robustness of our results to model misspecification, through analysis of predictive distributions for external data, and fitting data that are simulated under alternative observation models. The reconstructed ages of tree nodes are relatively robust, whereas posterior probabilities for topology are not reliable.

Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9868.2007.00648.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:70:y:2008:i:3:p:545-566

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().