删失数据下自回归模型的贝叶斯变量选择

Bayesian Variable Selection of Autoregressive Models with Censored Data

本文结合贝叶斯先验和删失数据自回归模型的拟似然,构造此类模型下的贝叶斯变量选择,通过连续spike和slab先验和0-1潜变量,运用EM算法得出有效的参数估计和变量选择方法,该方法计算量小且模型正确识别率高。在模拟研究和实证分析中验证了此方法的有效性。

The Bayesian prior and quasi-likelihood of autoregressive model with censored data were combined to construct Bayesian variable selection in this paper.We used EM algorithm to derive effective parameter estimation and variable selection approach by applying continuous spike and slab prior distribution and 0-1 latent variables.This method has the advantages of small amount of calculation and high correct recognition rate of the model.The effectiveness of this method is verified in Simulation study and empirical analysis.