Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data

TL;DR

This paper introduces Gaussian orthogonal latent factor processes to efficiently model and predict large correlated datasets, utilizing likelihood decomposition and Kalman filtering to handle computational challenges.

Contribution

It presents a novel approach combining orthogonal latent factors with likelihood decomposition and Kalman filtering for large-scale correlated data modeling.

Findings

Method performs well on simulated data

Method demonstrates superior prediction accuracy on real data

Posterior independence simplifies inference

Abstract

We introduce Gaussian orthogonal latent factor processes for modeling and predicting large correlated data. To handle the computational challenge, we first decompose the likelihood function of the Gaussian random field with a multi-dimensional input domain into a product of densities at the orthogonal components with lower-dimensional inputs. The continuous-time Kalman filter is implemented to compute the likelihood function efficiently without making approximations. We also show that the posterior distribution of the factor processes is independent, as a consequence of prior independence of factor processes and orthogonal factor loading matrix. For studies with large sample sizes, we propose a flexible way to model the mean, and we derive the marginal posterior distribution to solve identifiability issues in sampling these parameters. Both simulated and real data applications confirm the outstanding performance of this method.