Simulation-based diagnostics in random-coefficient models
Commonly applied diagnostic procedures in random-coefficient (multilevel) analysis are based on an inspection of the residuals, motivated by established procedures for ordinary regression. The deficiencies of such procedures are discussed and an alternative based on simulation from the fitted model (parametric bootstrap) is proposed. Although computationally intensive, the method proposed requires little programming effort additional to implementing the model fitting procedure. It can be tailored for specific kinds of outliers. Some computationally less demanding alternatives are described.