Sequential Monte Carlo EM for multivariate probit models

TL;DR

This paper introduces a sequential Monte Carlo EM algorithm for multivariate probit models, improving computational efficiency and handling dependence structures more effectively than traditional methods.

Contribution

It proposes a novel SMC sampler for truncated multivariate normals integrated into a fully sequential MCEM algorithm, reducing computational costs and enhancing model flexibility.

Findings

Efficiently estimates multivariate probit models with reduced computational cost.

Successfully applied to Six Cities dataset and simulated high-dimensional data.

Improves likelihood estimation by considering invariance in the model.

Abstract

Multivariate probit models (MPM) have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for the dependence modelling is the covariance matrix of an underlying latent multivariate Gaussian. Most approaches to MLE in multivariate probit regression rely on MCEM algorithms to avoid computationally intensive evaluations of multivariate normal orthant probabilities. As an alternative to the much used Gibbs sampler a new SMC sampler for truncated multivariate normals is proposed. The algorithm proceeds in two stages where samples are first drawn from truncated multivariate Student $t$ distributions and then further evolved towards a Gaussian. The sampler is then embedded in a MCEM algorithm. The sequential nature of SMC methods can be exploited to design a fully sequential version of the EM, where the samples are simply updated from one iteration to the next rather than resampled from scratch. Recycling the samples in this manner significantly reduces the computational cost. An alternative view of the standard conditional maximisation step provides the basis for an iterative procedure to fully perform the maximisation needed in the EM algorithm. The identifiability of MPM is also thoroughly discussed. In particular, the likelihood invariance can be embedded in the EM algorithm to ensure that constrained and unconstrained maximisation are equivalent. A simple iterative procedure is then derived for either maximisation which takes effectively no computational time. The method is validated by applying it to the widely analysed Six Cities dataset and on a higher dimensional simulated example. Previous approaches to the Six Cities overly restrict the parameter space but, by considering the correct invariance, the maximum likelihood is quite naturally improved when treating the full unrestricted model.