Robust indirect inference
Robust indirect inference
复制标题
DOI：
10.1198/016214503388619102
复制
发表时间：
2003-03-01
影响因子：
3.7
通讯作者：
Ronchetti, E
中科院分区：
文献类型：
--
作者：
Genton, MG;Ronchetti, E
在本文中，我们在一个统一的框架中开发了各种模型的鲁棒间接推理。研究了间接推理的局部鲁棒性，导出了间接估计量的影响函数，以及间接检验的水平和功率影响函数。然后使用这些工具来设计在与假设模型存在小偏差的情况下稳定的间接推理程序。虽然间接推理最初是为统计模型提出的，其可能性很难或甚至不可能计算和/或最大化，但我们在这里使用它作为一种设备来增强模型的估计器和测试，其中不可能或难以使用经典技术（如M估计器）。从金融应用，时间序列和空间统计的例子来说明。
In this article we develop robust indirect inference for a variety of models in a unified framework. We investigate the local robustness properties of indirect inference and derive the influence function of the indirect estimator, as well as the level and power influence functions of indirect tests. These tools are then used to design indirect inference procedures that are stable in the presence of small deviations from the assumed model. Although indirect inference was originally proposed for statistical models whose likelihood is difficult or even impossible to compute and/or to maximize, we use it here as a device to robustify the estimators and tests for models where this is not possible or is difficult with classical techniques such as M estimators. Examples from financial applications, time series, and spatial statistics are used for illustration.