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Title: Comparing estimation methods for generalized linear latent variable models Authors:  Sara Taskinen - University of Jyvaskyla (Finland) [presenting]
Francis Hui - The Australian National University (Australia)
Jenni Niku - University of Jyvaskyla (Finland)
David Warton - University of New South Wales (Australia)
Abstract: In many studies in community ecology, multivariate abundance data are often collected. Such data are characterized by two main features. First, the data are high-dimensional in that the number of species often exceeds the number of sites. Second, the data almost always cannot be suitably transformed to be normally distributed. Instead, the most common types of responses recorded include presence-absence records, overdispersed species counts, biomass, and heavily discretized percent cover data. One promising approach for modelling data described above is generalized linear latent variable models. By extending the standard generalized linear modelling framework to include latent variables, we can account for covariation between species not accounted for by the predictors, species interactions and correlations driven by missing covariates. We show how estimation and inference for the considered models can be performed efficiently using either the Laplace or the variational approximation method. We use simulations to study the finite-sample properties of the two approaches. Examples are used to illustrate the methods.