Bootstrap methods in computer simulation experiments
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This tutorial considers some very general procedures for analysing the results of a simulation experiment using bootstrap resampling. Bootstrapping has come to be recognised in statistics as being far ranging and effective. However it is not so well known in simulation despite being ideally suited for use in such a context. We discuss aspects ranging from the elementary to the advanced. We describe the rationale and the simple steps needed to implement bootstrapping in (i) estimation of the distributional properties of the output and its dependence on factors of interest; (ii) model fitting; (iii) model selection; (iv) model validation; (v) sensitivity analysis.