Random effects linear models for process mean and variance

We consider random effects models for both the mean and variance of a process where some variations in both the mean and variance can be explained by their related covariate effects. Empirical Bayes procedures are employed to estimate covariate effects. Approaches proposed are applied to evaluate the performance of the Phase V sensors of the tactical remote sensor system of the U.S. Marine Corps in terms of their mean and variance of detection distance with respect to target types and sensitivity levels of a sensor.