膨胀自举
Dilation bootstrap
摘要：
我们提出了一种方法来构造置信区域的一般形式的部分识别模型。该区域是通过对计量经济学结构的内部一致性检验进行反演得到的。我们发展了一种膨胀自举方法来处理抽样的不确定性，而不参考假设的经济结构。它需要引导的分位数过程的单变量数据和后者的一个新的推广到更高的维度。一旦选择膨胀来控制置信水平，观测数据的未知真实分布就可以被已知的经验分布所取代，然后可以获得置信区域，如Galichon和Henry（2011）以及Beresteanu，Molchanov和Molinari（2011）。
英文摘要：
We propose a methodology for constructing confidence regions with partially identified models of general form. The region is obtained by inverting a test of internal consistency of the econometric structure. We develop a dilation bootstrap methodology to deal with sampling uncertainty without reference to the hypothesized economic structure. It requires bootstrapping the quantile process for univariate data and a novel generalization of the latter to higher dimensions. Once the dilation is chosen to control the confidence level, the unknown true distribution of the observed data can be replaced by the known empirical distribution and confidence regions can then be obtained as in Galichon and Henry (2011) and Beresteanu, Molchanov and Molinari (2011).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
