Fast construction of optimal composite likelihoods

TL;DR

This paper introduces a fast, statistically justified method for constructing sparse composite likelihoods by selecting the most informative components, balancing statistical efficiency and computational cost.

Contribution

It proposes a novel procedure to select and combine low-dimensional likelihoods that optimizes statistical accuracy while reducing computational complexity.

Findings

The method achieves near-optimal asymptotic variance.

It efficiently identifies informative likelihood components.

The approach is computationally faster than existing methods.

Abstract

A composite likelihood is a combination of low-dimensional likelihood objects useful in applications where the data have complex structure. Although composite likelihood construction is a crucial aspect influencing both computing and statistical properties of the resulting estimator, currently there does not seem to exist a universal rule to combine low-dimensional likelihood objects that is statistically justified and fast in execution. This paper develops a methodology to select and combine the most informative low-dimensional likelihoods from a large set of candidates while carrying out parameter estimation. The new procedure minimizes the distance between composite likelihood and full likelihood scores subject to a constraint representing the afforded computing cost. The selected composite likelihood is sparse in the sense that it contains a relatively small number of informative sub-likelihoods while the noisy terms are dropped. The resulting estimator is found to have asymptotic variance close to that of the minimum-variance estimator constructed using all the low-dimensional likelihoods.