Space-varying regression models: specifications and simulation
Space-varying regression models: specifications and simulation
复制标题
DOI：
10.1016/s0167-9473(02)00211-6
复制
发表时间：
2003-03-28
影响因子：
1.8
通讯作者：
Rue, H
中科院分区：
文献类型：
--
作者：
Gamerman, D;Moreira, ARB;Rue, H
空间变化回归模型是允许回归系数在空间中变化的标准线性模型的推广。空间结构由成对差分先验的多变量扩展来指定，从而能够合并相邻结构和容易的抽样方案。通过结合超参数的先验分布来执行贝叶斯推断。这种方法会导致难以处理的后验分布。推断是通过从后验分布中抽取样本来近似的。不同的采样方案是可用的，并且可以在MCMC算法中使用。它们在处理回归系数块的方式上基本上不同。方法不同，从对每个特定位置的系数向量进行采样，到通过分析积分完全消除所有回归系数。从计算、链自相关和由此产生的推理方面对这些方案进行了比较。结果用模拟数据进行了说明，并应用于实际数据集。还讨论了可以适应不同形式的空间结构的相关现有规范。文章最后做了几点一般性的评论。(C)2002 Elsevier Science B.V.保留所有权利。
Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors, thus enabling incorporation of neighboring structures and easy sampling schemes. Bayesian inference is performed by incorporation of a prior distribution for the hyperparameters. This approach leads to an untractable posterior distribution. Inference is approximated by drawing samples from the posterior distribution. Different sampling schemes are available and may be used in an MCMC algorithm. They basically differ in the way they handle blocks of regression coefficients. Approaches vary from sampling each location-specific vector of coefficients to complete elimination of all regression coefficients by analytical integration. These schemes are compared in terms of their computation, chain auto-correlation, and resulting inference. Results are illustrated with simulated data and applied to a real dataset. Related prior specifications that can accommodate the spatial structure in different forms are also discussed. The paper concludes with a few general remarks. (C) 2002 Elsevier Science B.V. All rights reserved.