Estimation of Markov Random Field and covariance models from high-dimensional
data represents a canonical problem that has received a lot of attention in the
literature. A key assumption, widely employed, is that of {\em sparsity} of the
underlying model. In this paper, we study the problem of estimating such models
exhibiting a more intricate structure comp
本文提出一种针对高维张量数据的估计和推断方法，通过假设数据遵循张量正态分布来简化精度矩阵的估计，使用交替迭代优化算法估计每个稀疏精度矩阵，并且提出一种去偏置统计推断方法来控制虚警率，实证结果证实了该方法在自闭症谱系障碍和广告点击分析等实际应用上的有效性，同时我们将其编码为一个名为 Tlasso 的公开的 R 包。