High-dimensional neuroimaging and spatiotemporal blocks often contain structured missingness from acquisition artifacts, preprocessing failures, and sensor dropout. Multiple imputation propagates uncertainty, but fully conditional specification methods such as multivariate imputation by chained equations (MICE) can be slow or unstable when block dimension is large and correlations are strong. A multivariate normal (MVN) working model provides a coherent posterior predictive target and an exact data augmentation sampler, but repeated covariance sampling and matrix factorizations become costly in large dimensions. We propose High-dimensional Imputation via covariance Mode and Chained Equations (HIMCE), a hybrid multiple-imputation procedure for continuous blocks. Relative to exact MVN data augmentation, HIMCE preserves the Gaussian conditional imputation law and propagates mean- parameter uncertainty through stochastic coefficient or local-ridge draws. In high-dimensional blocks, it approximates covariance uncertainty through covariance-mode updating, optionally with a scalar bridge; in small blocks, it can restore exact covariance uncertainty through a conditional inverse-Wishart refresh. We record the exact Bayesian reference sampler and prove fixed-dimensional posterior consistency and asymptotic equivalence of mode plug-in prediction in total variation. We also develop diagnostics based on randomized rank-cell probability integral transform (PIT), PIT-consistent empirical coverage, and marginal distribution overlays. In the primary spatial benchmark, HIMCE improves posterior-mean error relative to HIMA and screened MICE, runs at HIMA-like speed and below half the MICE runtime, and improves interval coverage over HIMA, although MICE remains better calibrated. A repeated low- dimensional NHANES illustration shows improved coverage with competitive point prediction.

翻译：高维神经影像与时空数据块常因采集伪影、预处理失败及传感器缺失产生结构化缺失模式。多重插补可传递不确定性，但当块维度过高且变量间存在强相关时，链式方程多元插补（MICE）等完全条件规格方法会显著降低计算速度并引发数值不稳定。多元正态（MVN）工作模型虽能提供自洽的后验预测目标与精确数据增广采样器，但重复的协方差采样与矩阵分解在大维度场景下计算成本高昂。本文提出高维协方差模式与链式方程插补（HIMCE）——一种面向连续数据块的混合多重插补框架。相较于精确MVN数据增广，HIMCE保留了高斯条件插补法则，通过随机系数或局部岭回归抽取传播均值参数的不确定性。针对高维数据块，该方法采用协方差模式更新（可选配标量桥接）近似协方差不确定性；对于低维数据块，则通过条件逆Wishart刷新恢复完整协方差不确定性。我们解析记录了精确贝叶斯参考采样器，证明了固定维度的后验一致性以及模式插件预测在全变差度量下的渐近等价性。同时开发了基于随机化秩-单元格概率积分变换（PIT）、PIT一致性经验覆盖率及边际分布叠加的诊断工具。在核心空间基准测试中，HIMCE相比HIMA和筛选式MICE提升了后验均值误差，运行速度与HIMA相当且低于MICE运行时间的一半，并在区间覆盖率上优于HIMA，但MICE仍保持更优的校准效果。重复低维NHANES数据展示案例表明，该方法在保持竞争力点预测的同时改善了区间覆盖率。