论文标题
将空间数据的逆回归切成逆回归
Sliced Inverse Regression for Spatial Data
论文作者
论文摘要
切成薄片的逆回归是最流行的足够降低方法之一。最初，它是为独立和相同分布的数据而设计的，最近扩展到了串行和空间依赖数据的情况。在这项工作中，我们将其扩展到空间依赖的数据的情况下，当观测值对网格样结构进行时，响应也可能取决于邻近的协变量，因为在计量经济学空间回归应用中通常是这种情况。我们建议有关如何决定关注子空间的维度以及在对响应进行建模时可能会引起哪些空间滞后的指南。这些指南得到了进行的模拟研究的支持。
Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recently extend to the case of serially and spatially dependent data. In this work we extend it to the case of spatially dependent data where the response might depend also on neighbouring covariates when the observations are taken on a grid-like structure as it is often the case in econometric spatial regression applications. We suggest guidelines on how to decide upon the dimension of the subspace of interest and also which spatial lag might be of interest when modeling the response. These guidelines are supported by a conducted simulation study.
