A pairwise likelihood approach to generalized linear models with crossed random effects

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AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, background): “…of working also in non-standard settings, such as when the parameter under the null hypothesis lies on the boundary of the parametric space (Bellio and Varin (2005)), but it has the drawback of requiring the complete specification of a joint model for the data, thus losing in model…”
Double hierarchical generalized linear models (with discussion) (2006, methodology): “This covers the missing data problem by taking the missingness indicator as a binary response: see Lee et al. (2005) for shared random-effect models and Yun and Lee (2004b) for selection models.”
A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered-response model (2010, methodology): “Almost all earlier research efforts employing the CML technique have used the pairwise approach, including Apanasovich et al. (2008), Bellio and Varin (2005), de Leon (2005), Varin and Vidoni (2009), Varin et al. (2005), and Engle et al. (2007).”