ABSTRACT
In the previous three chapters we reviewed likelihood inferences about fixed parameters. There have been several attempts to extend Fisher’s likelihood beyond its use in parametric inference to inference from more general models that include unobserved random variables. Special cases are inferences for random effect models, prediction of unobserved future observations and missing data problems. We want to extend classical likelihood inferences about fixed parameters to those about random parameters or combinations of fixed and random parameters. This chapter aims to illustrate that such an extension is possible and useful for statistical modelling and inference.
