Dependent Dirichlet processes via thinning

TL;DR

This paper introduces a dependent Dirichlet process framework using thinning, enabling flexible modeling of multiple data sources by capturing shared and unique features, with improved inference accuracy demonstrated through simulations and real data application.

Contribution

It presents a novel dependent Dirichlet process construction via thinning, allowing for flexible dependence structures and efficient inference methods, advancing nonparametric Bayesian modeling.

Findings

Reduces uncertainty in group-specific inferences.

Prevents excessive information borrowing when unnecessary.

Outperforms state-of-the-art models in accuracy.

Abstract

When analyzing data from multiple sources, it is often convenient to strike a careful balance between two goals: capturing the heterogeneity of the samples and sharing information across them. We introduce a novel framework to model a collection of samples using dependent Dirichlet processes constructed through a thinning mechanism. The proposed approach modifies the stick-breaking representation of the Dirichlet process by thinning, that is, setting equal to zero a random subset of the beta random variables used in the original construction. This results in a collection of dependent random distributions that exhibit both shared and unique atoms, with the shared ones assigned distinct weights in each distribution. The generality of the construction allows expressing a wide variety of dependence structures among the elements of the generated random vectors. Moreover, its simplicity facilitates the characterization of several theoretical properties and the derivation of efficient computational methods for posterior inference. A simulation study illustrates how a modeling approach based on the proposed process reduces uncertainty in group-specific inferences while preventing excessive borrowing of information when the data indicate it is unnecessary. This added flexibility improves the accuracy of posterior inference, outperforming related state-of-the-art models. An application to the Collaborative Perinatal Project data highlights the model's capability to estimate group-specific densities and uncover a meaningful partition of the observations, both within and across samples, providing valuable insights into the underlying data structure.