A robust pairwise likelihood method for incomplete longitudinal binary data arising in clusters

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On the robustness of maximum composite likelihood estimate (2011, unknown): “Recent papers discussing this include Yi et al. (2011), Molenberghs et al. (2011) and He and Yi (2011).”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “While this is stated in many papers and should be rather obvious, we provide a formal proof of the unbiasedness of the CML score equations (see also Yi et al., 2011).”
A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling (2014, methodology): “…normally distributed (this is because of the unbiasedness of the CML score function, which is a linear combination of proper score functions associated with the marginal event probabilities forming the composite likelihood; for a formal proof, see Yi et al., 2011 and Xu and Reid, 2011).”
AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, unknown): “In some models the mechanism for missingness does not need to be modelled in the composite likelihood approach (Yi et al., 2009; Yi and He, 2010).”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, methodology): “The properties of the general CML estimator may be derived using the theory of estimating equations (see Cox and Reid, 2004, Yi et al., 2011).”