Pairwise likelihood estimation of latent autoregressive count models. (November 2020)

• Record Type: Journal Article
• Authors: Pedeli, Xanthi; Varin, Cristiano
• Is Part Of: Statistical methods in medical research. Volume 29:Number 11(2020)
• Page Start: 3278
• Page End: 3293
• Publication Date: 2020-11
• Subjects: Infectious disease data -- latent autoregressive model -- numerical integration -- pairwise likelihood -- time series; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220924068
• Languages: English
• ISSNs: 0962-2802

Abstract

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.