CMStatistics 2021: Start Registration
View Submission - CMStatistics
Title: Latent variable modelling in the number of kinds problem Authors:  Simon Wilson - Trinity College Dublin (Ireland) [presenting]
Asmaa Al-Ghamdi - Trinity College Dublin (Ireland)
Abstract: Data in a number of problems are usually modelled as one of 2 types: complete sampling data, where the number of individuals sampled and their kind are observed, and temporal data that describe when new kinds were observed but lack information on numbers sampled. Very different models and estimation methods apply to these types. Inconveniently, one of the most important applications of this problem, estimation of the number of species, falls into neither type; complete sampling information is lacking, but there is some proxy information on it (typically some measure of effort like estimates of numbers of individuals that can be sampled). We propose a hybrid model that allows such proxy information to be incorporated. The advantage of this approach is that it produces a framework around which the uncertainties in the number of species estimation can be modelled and quantified, something that is certainly needed for a question where estimates vary by at least an order of magnitude and estimates of uncertainty are often lacking. The inference is implemented via ABC and applied to 2 large databases: Catalogue of Life and World Register of Marine Species. Prior sensitivity and approaches to speeding up the implementation are discussed.