A survey of recent developments in the theory and application of composite likelihood is
provided, building on the review paper of Varin (2008). A range of application areas …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

BackgroundParticipant drop-out occurs in all longitudinal studies, and if systematic, may
lead to selection biases and erroneous conclusions being drawn from a study. AimsWe …

Composite marginal likelihoods are pseudolikelihoods constructed by compounding
marginal densities. In several applications, they are convenient surrogates for the ordinary …

Longitudinal studies often incur several problems that challenge standard statistical
methods for data analysis. These problems include non-ignorable missing data in …

Clustered longitudinal data feature cross‐sectional associations within clusters, serial
dependence within subjects, and associations between responses at different time points …

A common problem in the longitudinal data analysis is the missing data problem. Two types
of missing patterns are generally considered in statistical literature: monotone and non …

In statistical practice, incomplete measurement sequences are the rule rather than the
exception. Fortunately, in a large variety of settings, the stochastic mechanism governing the …

Researchers are often faced with the challenge of developing statistical models with
incomplete data. Exacerbating this situation is the possibility that either the researcher's …

We propose a marginal modeling approach to estimate the association between a time‐
dependent covariate and an outcome in longitudinal studies where some study participants …