Applications of the Bootstrap in Indirect Inference and Scenario Generation

小行星9813097 Bootstrap是一种计算推理的方法，它通过从历史数据中进行替换来创建称为Bootstrap样本的伪数据。 本研究将使用自举法将一个历史输入序列转换为许多相似的样本，从而允许使用独立的复制进行输出分析。 调查将包括移动块引导，这是时间序列数据的标准引导变量，和一个新的替代方案，阈值引导，这似乎很好地适应了间接推理的要求。 这种方法被称为间接推理，因为自助法不是直接应用于输出序列，而是直接通过输入序列应用。 传统的直接推理集中在从自助样本导出的估计量的性质（例如，无偏性，一致性），而间接推理侧重于样本本身的属性。 在这方面，自助样本最重要的品质是，它们在功能上与相同随机过程的独立样本不可区分。 如果成功的话，这项研究的一个好处是将可用输入的数量扩展到跟踪驱动的模拟，通过使用实际的历史输入序列驱动系统模型来实现真实感。 另一个好处是自动生成逼真的场景，用于财务规划中的风险评估和培训人类操作员进行复杂的决策任务。

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.