论文标题
使用潜在稀疏条件高斯混合物进行多变量混合结局的半参数插补
Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes
论文作者
论文摘要
本文提出了一种使用条件高斯混合物的柔性贝叶斯方法来进行多个插补。我们介绍了在混合模型中用于协变量依赖性混合比例的新型收缩先验，以自动选择插图步骤中使用的合适数量的组件。我们开发了一种有效的采样算法，用于后验计算，并通过马尔可夫链蒙特卡洛方法进行多次插补。所提出的方法可以轻松地扩展到数据不仅包含连续变量，还包含离散变量（例如二进制和计数值）的情况。我们还建议通过扩展基于自举的贝叶斯推断的完整数据来扩展基于后验预测分布的损失函数定义的参数的近似贝叶斯推断。通过使用模拟和实际数据的数值研究来证明所提出的方法。
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing proportions in the mixture models to automatically select the suitable number of components used in the imputation step. We develop an efficient sampling algorithm for posterior computation and multiple imputation via Markov Chain Monte Carlo methods. The proposed method can be easily extended to the situation where the data contains not only continuous variables but also discrete variables such as binary and count values. We also propose approximate Bayesian inference for parameters defined by loss functions based on posterior predictive distributing of missing observations, by extending bootstrap-based Bayesian inference for complete data. The proposed method is demonstrated through numerical studies using simulated and real data.
