Details of talk
|Title||Directional Mixed Effects Models for Compositional Data|
|Presenter||Janice Scealy (Australian National University)|
Compositional data are vectors of proportions defined on the unit simplex and this type of constrained data occur frequently in applications. It is also possible for the compositional data to be correlated due to the clustering or grouping of the observations. We propose a new class of mixed model for compositional data based on the Kent distribution for directional data, where the random effects also have Kent distributions. The advantage of this approach is that it handles zero components directly and the new model has a fully flexible underlying covariance structure. One useful property of the new directional mixed model is that the marginal mean direction has a closed form and is interpretable. The random effects enter the model in a multiplicative way via the product of a set of rotation matrices and the conditional mean direction is a random rotation of the marginal mean direction. For estimation we apply a quasi-likelihood method which results in solving a new set of generalised estimating equations and these are shown to have low bias in typical situations. For inference we use a nonparametric bootstrap method for clustered data which does not rely on estimates of the shape parameters (shape parameters are difficult to estimate in Kent models). The new approach is shown to be more tractable than the traditional approach based on the logratio transformation.