A hierarchical Bayesian approach for handling missing classification data.

ABSTRACT: Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these "partial" observations must be modified to include the missing data mechanism to avoid spurious inference.We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in c

SUBMITTER: Ketz AC 

PROVIDER: S-EPMC6434567 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature