Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Bayesian Posterior Predictive Checks for Complex Models

In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specifically, as well as a Bayesian approach to modeling more generally, in tackling these issues. First, they review the Bayesian...

Model Building for Nonlinear Mixed-Effects Models

Nonlinear mixed-effects models involve both fixed effects and random effects. Model building for nonlinear mixed-effects models is the process of determining the characteristics of both the fixed and the random effects so as to give an adequate but parsimonious model. We describe procedures based on information criterion statistics for comparing different structures of the random effects compon...

Nonlinear mixed effects models for longitudinal data

Nonlinear mixed-effects modeling: individualization and prediction.

The development of biomathematical models for the prediction of fatigue and performance relies on statistical techniques to analyze experimental data and model simulations. Statistical models of empirical data have adjustable parameters with a priori unknown values. Interindividual variability in estimates of those values requires a form of smoothing. This traditionally consists of averaging ob...

Dirichlet Processes in Nonlinear Mixed Effects Models

Two efficient approaches are considered to deal with the difficulty in computing the intractable integrals when implementing Gibbs sampling in the nonlinear mixed effects model (NLMM) based on Dirichlet processes (DP). The first approach computes the Laplace's approximation to the integral for its high accuracy, low cost, and ease of implementation. The second approach uses the no-gaps algorith...