Bayesian Observation Modeling in Presence-Only Data

• Activity Number: 88
• Type: Contributed
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Ioanna Manolopoulou*+ and Richard Hahn
• Companies: University College London and The University of Chicago, Booth School of Business
• Keywords: Sampling model ; Partial identifiability ; Non-parametric ; Species distribution

Abstract

The prevalence of presence-only samples eg. in ecology or criminology has led to a variety of statistical approaches. Aiming to predict ecological niches, species distribution models provide probability estimates of a binary response (presence/absence) in light of a set of environmental covariates. However, the associated challenges are confounded by non-uniform observation models; even in cases where observation is driven by seemingly irrelevant factors, these may distort estimates about the distribution of the species as a function of covariates due to unknown correlations. We present a Bayesian non-parametric approach to addressing sampling bias by carefully incorporating an observation model in a partially identifiable framework with selectively informative priors and linking it to the underlying process. Any available information about the role of various covariates in the observation process can then naturally enter the model. For example, in cases where sampling is driven by presumed likelihood of detecting a presence, the observation model becomes a proxy of the presence/absence model. We illustrate our methods on an example from species distribution modeling.