论文标题
不完整模型中的有限样本推断
Finite Sample Inference in Incomplete Models
论文作者
论文摘要
我们提出了不完整模型参数的置信区，并在有限样品中覆盖了真实参数。我们的置信区域扭转了一个测试，该测试将蒙特卡洛测试概括为不完整的模型。测试统计量是对尖锐确定区域的新最佳运输表征的离散类似物。测试统计量和临界值都取决于从潜在变量的分布中得出的仿真，并使用解决方案来分散最佳传输，因此可以进行线性编程问题进行计算。我们还基于参数的临界值，在参数空间中提出了一个快速的初步搜索，具有替代性，更保守但一致的测试。
We propose confidence regions for the parameters of incomplete models with exact coverage of the true parameter in finite samples. Our confidence region inverts a test, which generalizes Monte Carlo tests to incomplete models. The test statistic is a discrete analogue of a new optimal transport characterization of the sharp identified region. Both test statistic and critical values rely on simulation drawn from the distribution of latent variables and are computed using solutions to discrete optimal transport, hence linear programming problems. We also propose a fast preliminary search in the parameter space with an alternative, more conservative yet consistent test, based on a parameter free critical value.
