Informed Random Partition Models with Temporal Dependence

TL;DR

This paper introduces a flexible clustering method that incorporates varying levels of prior uncertainty for different data subsets, enhancing the integration of expert knowledge and data-driven insights in spatio-temporal analysis.

Contribution

It proposes a novel approach that assigns individual allocation probabilities, allowing for nuanced prior information incorporation without relying on partition penalties.

Findings

Improved prior specification flexibility demonstrated through simulations.

Effective modeling of spatio-temporal PM10 data in Germany.

Enhanced clustering accuracy with uncertain prior information.

Abstract

Model-based clustering is a powerful tool that is often used to discover hidden structure in data by grouping observational units that exhibit similar response values. Recently, clustering methods have been developed that permit incorporating an ``initial'' partition informed by expert opinion. Then, using some similarity criteria, partitions different from the initial one are down weighted, i.e. they are assigned reduced probabilities. These methods represent an exciting new direction of method development in clustering techniques. We add to this literature a method that very flexibly permits assigning varying levels of uncertainty to any subset of the partition. This is particularly useful in practice as there is rarely clear prior information with regards to the entire partition. Our approach is not based on partition penalties but considers individual allocation probabilities for each unit (e.g., locally weighted prior information). We illustrate the gains in prior specification flexibility via simulation studies and an application to a dataset concerning spatio-temporal evolution of ${\rm PM}_{10}$ measurements in Germany.