A Bayesian Method for the Dynamic Regression Analysis
A Bayesian Method for the Dynamic Regression Analysis
复制标题
动态回归分析的贝叶斯方法
DOI：
10.5687/iscie.8.8
复制
发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Xing
中科院分区：
文献类型：
--
作者：
Xing
对回归系数施加高斯随机差分方程形式的先验。超参数的估计和差分方程阶的确定是通过最大化超参数的边际似然和使用最小ABIC过程来确定的。时变回归系数的估计是通过最大化系数的后验密度来获得的。文中给出了一个数值算例和两个仿真研究，验证了该方法的准确性。应用该模型对四个国家钢铁消费与GNP的动态相关性进行了分析。
prior in the form of a Gaussian stochastic difference equation is imposed on the regression coefficient. The estimates of hyperparameters and the order of the difference equation are determined by maximizing marginal likelihood of the hyperparameters and using the minimum ABIC procedure. The estimate of the time varying regression coefficient is obtained by maximizing a posterior density of the coefficient. A numerical example and two simulation studies on the accuracy of the procedure are given. The model is applied to the analyses of the dynamic dependences of steel consumption on GNP for four countries.