论文标题
非参数增强概率加权与稀疏性
Nonparametric augmented probability weighting with sparsity
论文作者
论文摘要
实践中经常出现无响应，只是简单地忽略它可能会导致错误的推断。此外，收集的协变量的数量可能会随着现代统计数据的样本量而增加，因此参数插补或倾向得分加权通常会导致效率低下，而无需考虑稀疏性。在本文中，我们通过采用有效的基于内核的学习梯度算法来识别真正有用的协变量，提出了一种非参数归档方法，并通过稀疏学习。此外，采用了增强的概率加权框架来提高非参数归档方法的估计效率，并在规则性假设下建立了相应估计量的限制分布。几个模拟示例和一个现实生活分析也支持了所提出的方法的性能。
Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in modern statistics, so parametric imputation or propensity score weighting usually leads to inefficiency without consideration of sparsity. In this paper, we propose a nonparametric imputation method with sparse learning by employing an efficient kernel-based learning gradient algorithm to identify truly informative covariates. Moreover, an augmented probability weighting framework is adopted to improve the estimation efficiency of the nonparametric imputation method and establish the limiting distribution of the corresponding estimator under regularity assumptions. The performance of the proposed method is also supported by several simulated examples and one real-life analysis.
