In this paper, a highly effective Bayesian sampling algorithm based on auxiliary variables is used to estimate the graded response model with non-ignorable missing response data. Compared with the traditional marginal likelihood method and other Bayesian algorithms, the advantages of the new algorithm are discussed in detail. Based on the Markov Chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarignal likelihood are employed to compare the different missing mechanism models. Two simulation studies are conducted and a detailed analysis of the sexual compulsivity scale data is carried out to further illustrate the proposed methodology.

本文使用基于辅助变量的高效贝叶斯采样算法来估计具有不可忽略的缺失响应数据的分级响应模型。与传统的边缘似然法和其他贝叶斯算法相比，详细讨论了新算法的优点。基于来自后验分布的马尔可夫链蒙特卡洛样本，采用偏差信息准则和拟海洋边缘似然的对数来比较不同的缺失机制模型。进行了两次模拟研究，并对性强迫量表数据进行了详细分析，以进一步说明所提出的方法。