论文标题
使用重新采样统计的依赖性推理
Dependence-Robust Inference Using Resampled Statistics
论文作者
论文摘要
我们开发了对弱依赖性的一般形式的推理程序。该过程利用通过重新采样构建的测试统计信息，以不取决于数据的相关结构的方式。我们证明，在弱需求下，统计量在参数速率上可以始终如一地估算统计数字。这适用于许多众所周知的依赖形式下的常规估计量，并证明了依赖性的主张。我们将应用程序应用于具有未知或复杂形式的依赖形式的设置，并以各种形式的网络依赖性为主要示例。我们在瞬间平等和不平等方面开发测试。
We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.
