Ancestral Inference from Functional Data: Statistical Methods and Numerical Examples
This work addresses the challenge of ancestral inference for continuous functional data in evolutionary biology, representing an incremental advancement by applying existing methods to a new type of data.
The paper tackled the problem of inferring ancestral biological traits that are continuous functions rather than scalar variables, using phylogenetic Gaussian process regression and independent principal component analysis on simulated data to estimate ancestral function-valued traits and evolutionary parameters.
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent principal component analysis (IPCA) as a method for dimension reduction, we construct distributional estimates of ancestral function-valued traits, and estimate parameters describing their evolutionary dynamics.