ML, PL, QL in Markov chain models

TL;DR

This paper compares maximum likelihood, pseudo-likelihood, and quasi-likelihood methods for Markov chain models, highlighting the advantages of QL in robustness and performance through theoretical and practical analysis.

Contribution

It provides limiting normality results and performance comparisons of ML, PL, and QL methods specifically for general Markov chain models.

Findings

QL typically outperforms PL in robustness and efficiency.

QL loses very little to ML in terms of performance.

Methods are illustrated with DNA sequence evolution models.

Abstract

In many spatial and spatial-temporal models, and more generally in models with complex dependencies, it may be too difficult to carry out full maximum likelihood (ML) analysis. Remedies include the use of pseudo-likelihood (PL) and quasi-likelihood (QL) (also called the composite likelihood). The present article studies the ML, the PL and the QL methods for general Markov chain models, partly motivated by the desire to understand the precise behaviour of PL and QL methods in settings where this can be analysed. We present limiting normality results and compare performances in different settings. The PL and QL methods can be seen as maximum penalised likelihood methods. We find that the QL strategy is typically preferable to the PL, and that it loses very little to the ML, while earning in model robustness. It has also appeal and potential as a modelling tool. Our methods are illustrated for analysis of DNA sequence evolution type models.