Screening the Many Inputs of Realistic Simulation Models

Jack Kleijnen ()

Chapter 4 in Design and Analysis of Simulation Experiments, 2015, pp 135-178 from Springer

Abstract: Abstract This chapter is organized as follows. Section 4.1 introduces “screening” defined as searching for the really important inputs in experiments with simulation models that have “very many” inputs (say, hundreds of inputs); this section also gives an overview of several screening methods. Section 4.2 explains a screening method called sequential bifurcation (SB); for simplicity, this section assumes deterministic simulation and first-order polynomial metamodels. Section 4.3 explains SB for deterministic simulations and second-order polynomial metamodels that satisfy the “heredity” assumption; this assumption states that if a specific input has no first-order effect, then this input has no second-order effects either. Section 4.4 explains SB for random simulations with a fixed number of replications per input combination. Section 4.5 explains SB for random simulations with a variable number of replications determined through Wald’s sequential probability ratio test (SPRT). Section 4.6 discusses multiresponse sequential bifurcation (MSB), which extends SB to problems with multiple types of simulation responses (multivariate output). Section 4.7 discusses validation of the SB and MSB assumptions. Section 4.8 summarizes the major conclusions of this chapter. The chapter ends with solutions for the exercises, and a list with references for further study.

Keywords: Simulation Output; Important Input; Output Type; Input Combination; Monte Carlo Experiment (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-18087-8_4

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