Estimating a mixing distribution on the sphere using predictive recursion

TL;DR

This paper introduces a computationally efficient predictive recursion method for estimating smooth mixing distributions on the sphere, addressing a gap in directional data analysis and demonstrating its advantages over likelihood-based approaches.

Contribution

It develops and validates a novel predictive recursion algorithm for spherical mixture models, capable of estimating smooth mixing densities and supporting practical applications.

Findings

PR is computationally efficient and supports smooth density estimation.

PR shows asymptotic consistency in spherical mixture models.

Simulation and real-data examples demonstrate PR's advantages over likelihood-based methods.

Abstract

Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent variable responsible for that heterogeneity. This is a common problem for data taking values in a Euclidean space, but the work on mixing distribution estimation based on directional data taking values on the unit sphere is limited. In this paper, we propose using the predictive recursion (PR) algorithm to solve for a mixture on a sphere. One key feature of PR is its computational efficiency. Moreover, compared to likelihood-based methods that only support finite mixing distribution estimates, PR is able to estimate a smooth mixing density. PR's asymptotic consistency in spherical mixture models is established, and simulation results showcase its benefits compared to existing likelihood-based methods. We also show two real-data examples to illustrate how PR can be used for goodness-of-fit testing and clustering.