论文标题
具有缺失值的张量的增强回归模型
An Augmented Regression Model for Tensors with Missing Values
论文作者
论文摘要
异类但互补的数据来源为开发系统的准确统计模型提供了前所未有的机会。尽管现有方法显示出令人鼓舞的结果，但它们主要适用于以其完整形式测量系统输出的情况。但是，实际上，获得系统的完整输出测量可能是不可行的，这会导致包含缺失值的观测值。本文介绍了一个通用框架，该框架将张量回归与张量的完成集成在一起，并提出了一个有效的优化框架，该框架在两个步骤之间交替以进行参数估计。通过多个模拟和案例研究，我们评估了提出的方法的性能。结果表明，与基准相比，提出的方法的优越性。
Heterogeneous but complementary sources of data provide an unprecedented opportunity for developing accurate statistical models of systems. Although the existing methods have shown promising results, they are mostly applicable to situations where the system output is measured in its complete form. In reality, however, it may not be feasible to obtain the complete output measurement of a system, which results in observations that contain missing values. This paper introduces a general framework that integrates tensor regression with tensor completion and proposes an efficient optimization framework that alternates between two steps for parameter estimation. Through multiple simulations and a case study, we evaluate the performance of the proposed method. The results indicate the superiority of the proposed method in comparison to a benchmark.
