论文标题
部分可观测时空混沌系统的无模型预测
A Double Robust Approach for Non-Monotone Missingness in Multi-Stage Data
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Multivariate missingness with a non-monotone missing pattern is complicated to deal with in empirical studies. The traditional Missing at Random (MAR) assumption is difficult to justify in such cases. Previous studies have strengthened the MAR assumption, suggesting that the missing mechanism of any variable is random when conditioned on a uniform set of fully observed variables. However, empirical evidence indicates that this assumption may be violated for variables collected at different stages. This paper proposes a new MAR-type assumption that fits non-monotone missing scenarios involving multi-stage variables. Based on this assumption, we construct an Augmented Inverse Probability Weighted GMM (AIPW-GMM) estimator. This estimator features an asymmetric format for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. We apply this method to cases of missingness in both endogenous regressor and outcome, using the Oregon Health Insurance Experiment as an example. We check the correlation between missing probabilities and partially observed variables to justify the assumption. Moreover, we find that excluding incomplete data results in a loss of efficiency and insignificant estimators. The proposed estimator reduces the standard error by more than 50% for the estimated effects of the Oregon Health Plan on the elderly.
