Time-calibrated phylogenies have become essential to evolutionary biology. A recurrent and unresolved question for dating analyses is whether genes with missing data cells should be included or excluded. This issue is particularly unclear for the most widely used dating method, the uncorrelated lognormal approach implemented in BEAST. Here, we test the robustness of this method to missing data....

— We extend our recently developed Markov chain Monte Carlo algorithm for Bayesian estimation of species divergence times to allow variable evolutionary rates among lineages. The method can use heterogeneous data from multiple gene loci and accommodate multiple fossil calibrations. Uncertainties in fossil calibrations are described using flexible statistical distributions. The prior for diverge...

We introduce estimation and test procedures through divergence minimization for models satisfying linear constraints with unknown parameter. Several statistical examples and motivations are given. These procedures extend the empirical likelihood (EL) method and share common features with generalized empirical likelihood (GEL). We treat the problems of existence and characterization of the diver...

In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback-Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback-Leibler divergence (DC-KL), to reduce the contribution of the...

Bayesian inference provides a powerful framework for integrating different sources of information (in particular, molecules and fossils) to derive estimates of species divergence times. Indeed, it is currently the only framework that can adequately account for uncertainties in fossil calibrations. We use 2 Bayesian Markov chain Monte Carlo programs, MULTIDIVTIME and MCMCTREE, to analyze 3 empir...

Heterotachy or variation of rates-across-sites and lineages is widespread (e.g., Fitch and Markowitz, 1970; Uzzel and Corbin, 1971; Lopez et al., 2002) and can cause biased tree estimation (Lockhart et al., 1998; Susko et al., 2004; Inagaki et al., 2004). Kolaczkowski and Thornton (2004) described an intriguing heterotachy model that lead to topological bias for likelihood-based methods. That w...