摘要：据JoE官网显示，来自云南大学的Jinhan Xie、西安交通大学的严晓东、阿尔伯塔大学的Bei Jiang和Linglong Kong，合作撰写的论文“Statistical inference for smoothed quantile regressio
据JoE官网显示，来自云南大学的Jinhan Xie、西安交通大学的严晓东、阿尔伯塔大学的Bei Jiang和Linglong Kong，合作撰写的论文“Statistical inference for smoothed quantile regression with streaming data”，在国际计量经济学顶级期刊《Journal of Econometrics》线上正式发表。
Title: Statistical inference for smoothed quantile regression with streaming data
对流数据进行平滑分位数回归的统计推断
作者简介
Jinhan Xie
云南大学
严晓东
西安交通大学
Bei Jiang
阿尔伯塔大学
Linglong Kong
阿尔伯塔大学
In this paper, we tackle the problem of conducting valid statistical inference for quantile regression with streaming data. The main difficulties are that the quantile regression loss function is non-smooth and it is often infeasible to store the entire dataset in memory, rendering traditional methodologies ineffective. We introduce a fully online updating method for statistical inference in smoothed quantile regression with streaming data to overcome these issues. Our main contributions are twofold. First, for low-dimensional data, we present an incremental updating algorithm to obtain the smoothed quantile regression estimator with the streaming data set. The proposed estimator allows us to construct asymptotically exact statistical inference procedures. Second, within the realm of high-dimensional data, we develop an online debiased lasso procedure to accommodate the special sparse structure of streaming data. The proposed online debiased approach is updated with only the current data and summary statistics of historical data and corrects an approximation error term from online updating with streaming data. Furthermore, theoretical results such as estimation consistency and asymptotic normality are established to justify its validity in both settings. Our findings are supported by simulation studies and illustrated through applications to Seoul’s bike-sharing demand data and index fund data.在本文中，我们解决了对流数据进行分位数回归的统计推断问题。主要难点在于分位数回归损失函数是非平滑的，而且通常不可能将整个数据集存储在内存中，这使得传统方法失效。我们引入了一种完全在线更新方法，用于流数据中平滑分位数回归的统计推断，以克服这些问题。我们的主要贡献有两个方面。首先，对于低维数据，我们提出了一个增量更新算法，以获得流数据集的平滑分位数回归估计器。所提出的估计器使我们能够构建渐近精确的统计推断程序。其次，在高维数据领域，我们开发了一种在线去偏lasso程序，以适应流数据的特殊稀疏结构。所提出的在线去偏方法仅使用当前数据和历史数据的汇总统计信息进行更新，并校正了在线更新中流数据的近似误差项。此外，我们还建立了估计一致性和渐近正态性等理论结果，以证明其在这两种设置中的有效性。我们的发现得到了模拟研究的支持，并通过首尔自行车共享需求数据和指数基金数据的应用得到了说明。
来源：学术圈
