9813097 Willemain The bootstrap is a method of computational inference that creates pseudodata called bootstrap samples by resampling with replacement from the historical data. This research will use the bootstrap to convert one historical input series into many similar samples, thus allowing the use of independent replications for output analysis. The investigation will include both the moving blocks bootstrap, which is the standard bootstrap variant for time series data, and a new alternative, the threshold bootstrap, that seems well-adapted to the demands of indirect inference. This approach is referred to as indirect inference because the bootstrap is not applied directly to the output series but instead is applied directly via the input series. Traditional direct inference focuses on the properties of estimators derived from bootstrap samples (e.g., unbiasedness, consistency), whereas indirect inference focuses on the properties of the samples themselves. In this regard, the most important quality of the bootstrap samples is that they be functionally indistinguishable from independent samples of the same stochastic process. If successful, one benefit of this research would be expanding the number of available inputs to trace-driven simulations, which achieve realism by driving system models with actual historical input series. Another benefit would be the automatic generation of lifelike scenarios for risk assessment in financial planning and for training human operators in complex decision making tasks.
