论文标题
野生引导性推断纵向数据的惩罚分位数回归
Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data
论文作者
论文摘要
现有的纵向数据分位数回归理论主要集中在点估计上。在这项工作中，我们研究了统计推断。我们提出了一种野生残留的自举程序，并表明它在近似惩罚估计器的分布上渐近有效。该模型对个人效应没有任何限制，并且估计器通过渐近地收缩衰减而实现一致性。新方法易于实施，模拟研究表明，与现有程序相比，它具有准确的小样本行为。最后，我们使用美国人口普查数据来说明新方法，以估算一个包括超过八万个参数的模型。
The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.
