Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis...

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys classic and modern designs for experiments with simulation models.Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc.These designs...

We present a Bayesian estimation method applied to an extended set of national accounts data and estimates of approximately 2500 variables. The method is based on conventional national accounts frameworks as compiled by countries in Central America, in particular Guatemala, and on concepts that...

This introductory tutorial gives a survey on the use of statistical designs for what if-or sensitivity analysis in simulation.This analysis uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also...

Abstract: Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing...

Sensitivity analysis in investment problems is an important tool to determine which factors can jeopardize the future of the investment.Information on the probability distribution of those factors that affect the investment is mostly lacking.In those situations the analysts have two options: (i)...