We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inference. Such a prior is a density or relative density that weights an observed likelihood function, leading to the elimination of parameters that are not of interest and then a density-type...

The inverse probability weighted generalized estimating equations approach (Robins et al. 1994; Robins et al. 1995), effectively removes bias and provides valid statistical inference for regression parameter estimation in marginal models when longitudinal data contain missing values. The validity of...

Semiparametric nonlinear mixed-effects (NLME) models are very flexible in modeling long-term HIV viral dynamics. In practice, statistical analyses are often complicated due to measurement errors and missing data in covariates and non-ignorable missing data in the responses. We consider...