A1185
Title: Autocompound random measures
Authors: Riccardo Corradin - University of Milano-Bioccca (Italy) [presenting]
Fabrizio Leisen - Kings College London (United Kingdom)
Abstract: In statistical analysis, quantities that depend on specific observational times are commonly observed. A class of time-dependent nonparametric models is introduced, suited for scenarios involving populations observed at distinct discrete times. Starting with an ancestral measure, it is possible to derive a time-specific random measure by acting on its intensity function, in the spirit of compound random measures. The resulting family of models exhibits desirable properties, including mathematical tractability, simple expressions for its main summaries, and a closed-form representation of the joint posterior distribution at distinct observed times. Such a model can be used as a building block for dynamic population studies, defining tractable species sampling models that evolve over time, or convolved with a kernel function to obtain time-dependent mixture models.
