Screening for Important Inputs by Bootstrapping
Seminar Room 1, Newton Institute
We consider resampling techniques in two contexts involving uncertainty in input modelling. The first concerns the fitting of input models to input data. This is a problem of estimation and can be treated either parametrically or non-parametrically. In either case the problem of assessing uncertainty in the fitted input model arises. We discuss how resampling can be used to deal with this. The second problem concerns the situation where the simulation output depends on a large number input variables and the problem is to identify which input variables are important in influencing output behaviour. Again we discuss how resampling can be used to handle this problem. An interesting aspect of both problems is that the replications used in the resampling involved in both problems are mutually independent. This means that greatly increased processing speed is possible if replications can be carried out in parallel. Recent developments in computer architecture makes parallel implementation much more readily available. This has a particularly interesting consequence for handling input uncertainty when simulation is used in real time decision taking, where processing speed is paramount. We discuss this aspect especially in the context of real time system improvement, if not real time optimization.