标题： 空间回归与贝叶斯滤波器 显示英文标题

标题： Spatial Regression and the Bayesian Filter

摘要： 空间相关结果的回归带来了许多挑战，无论是推断还是计算方面都存在困难。 非空间模型和传统的空间混合效应模型各有优缺点，这使得从业者难以确定如何开展空间回归分析。 我们讨论了各种流行的空间回归模型隐含的数据生成机制，并探讨了这些假设的影响。 我们提出贝叶斯空间过滤作为非空间模型与传统空间混合模型之间的近似中间方法。 通过模拟，我们展示了我们的贝叶斯空间过滤模型具有若干理想的特性，因此可能是空间统计学家工具箱中的有用补充。 显示英文摘要

摘要： Regression for spatially dependent outcomes poses many challenges, for inference and for computation. Non-spatial models and traditional spatial mixed-effects models each have their advantages and disadvantages, making it difficult for practitioners to determine how to carry out a spatial regression analysis. We discuss the data-generating mechanisms implicitly assumed by various popular spatial regression models, and discuss the implications of these assumptions. We propose Bayesian spatial filtering as an approximate middle way between non-spatial models and traditional spatial mixed models. We show by simulation that our Bayesian spatial filtering model has several desirable properties and hence may be a useful addition to a spatial statistician's toolkit.