Selecting the best linear simulation metamodel

We consider the output of a simulation model of a system about which little is initially known. This output is often dependent on a large number of factors. It is helpful, in examining the behaviour of the system, to find a statistical metamodel containing only those factors most important in influencing this output. The problem is therefore one of selecting a parsimonious metamodel that includes only a subset of the factors, but which nevertheless adequately describes the behaviour of the output. The total number of possible submodels from which we are choosing grows exponentially with the number of factors, so a full examination of all possible submodels rapidly becomes intractable. We show how resampling can provide a simple solution to the problem, by allowing potentially good submodels to be rapidly identified. This resampling approach also allows a systematic statistical comparison of good submodels to be made.