Accounting for model uncertainty in seemingly unrelated regressions
Accounting for model uncertainty in seemingly unrelated regressions
复制标题
DOI：
10.1198/106186002475
复制
发表时间：
2002-09-01
影响因子：
2.4
通讯作者：
Mallick, BK
中科院分区：
文献类型：
--
作者：
Holmes, CC;Denison, DGT;Mallick, BK
本文考虑了贝叶斯似不相关回归（SUR）模型中的推理，其中假设回归变量的集合是先验未知的。也就是说，我们通过定义模型空间上的先验分布来允许协变量集的不确定性。后验推理分析是棘手的，我们采用计算机密集型模拟使用可变维马尔可夫链蒙特卡罗算法近似感兴趣的数量。给出了阶数未知的向量自回归（VAR）模型和结点未知的多元样条模型的应用。
This article considers inference in a Bayesian seemingly unrelated regression (SUR) model where the set of regressors is assumed unknown a priori. That is, we allow for uncertainty in the covariate set by defining a prior distribution on the model space. The posterior inference is analytically intractable and we adopt computer-intensive simulation using variable dimension Markov chain Monte Carlo algorithms to approximate quantities of interest. Applications are given for vector autoregression (VAR) models of unknown order and multivariate spline models with unknown knot points.