Meeting Statistics Seminar 2018: Temporal and Spatio-Temporal Random Partition Models

Relatore
Garritt Page - Assistant Professor - Brigham Young University, USA

Abstract
Data that are spatially referenced often represent an instantaneous point in time at which the spatial process is measured. Because of this it is becoming more common to consider spatio-temporal processes and accommodate both temporal and spatial dependence. We propose capturing the temporal evolution of dependent structures by modeling a sequence of partitions indexed by time jointly. We derive a few characteristics from the joint model and show how it impacts dependence at the observation level. Computational strategies are detailed and the method is applied to Chilean standardized testing scores.

Organizzazione
Massimo Ventrucci