论文标题
保证统计方法的全面模拟评估
Guarantees for Comprehensive Simulation Assessment of Statistical Methods
论文作者
论文摘要
仿真可以评估假设参数值网格上的I型错误，FDR或偏差等属性的统计方法。但是，网格点之间的差距呢？连续仿真扩展（CSE）是一个逐一模拟框架，可以用（1）在参数空间区域有效的置信频带补充模拟，或（2）校准拒绝阈值，以提供严格的I型I型误差控制的严格证明。 CSE使用与Renyi Divergence相关的模型移位将网格点的模拟估计扩展到附近空间的边界，我们将其分析指数族家族或规范GLM形式的模型。 CSE可以使用自适应采样，滋扰参数，管理审查，多臂，多个测试，贝叶斯随机化，贝叶斯决策和任意复杂性的推理算法。作为一个案例研究，我们对具有4个未知统计参数的II/III期贝叶斯选择设计进行校准，以控制II/III期贝叶斯选择设计。潜在的应用包括校准新的统计程序或简化适应性试验设计的监管审查。我们的开源软件实施烙印可提供Athttps：//github.com/confirm-solutions/imprint
Simulation can evaluate a statistical method for properties such as Type I Error, FDR, or bias on a grid of hypothesized parameter values. But what about the gaps between the grid-points? Continuous Simulation Extension (CSE) is a proof-by-simulation framework which can supplement simulations with (1) confidence bands valid over regions of parameter space or (2) calibration of rejection thresholds to provide rigorous proof of strong Type I Error control. CSE extends simulation estimates at grid-points into bounds over nearby space using a model shift bound related to the Renyi divergence, which we analyze for models in exponential family or canonical GLM form. CSE can work with adaptive sampling, nuisance parameters, administrative censoring, multiple arms, multiple testing, Bayesian randomization, Bayesian decision-making, and inference algorithms of arbitrary complexity. As a case study, we calibrate for strong Type I Error control a Phase II/III Bayesian selection design with 4 unknown statistical parameters. Potential applications include calibration of new statistical procedures or streamlining regulatory review of adaptive trial designs. Our open-source software implementation imprint is available athttps://github.com/Confirm-Solutions/imprint
