评价自回归过程处理效果的动态综合控制方法

IF 3.1 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-10-27 DOI:10.1093/jrsssb/qkad103

Xiangyu Zheng, Song Xi Chen

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引用次数: 0

摘要

摘要以评价空气污染预警对空气质量的影响为出发点，提出了一种自回归模型下具有时变混杂因素和空间依赖性的微观数据动态综合控制方法。我们采用经验似然来定义综合控制权值，保证了唯一解并允许理论分析。动态匹配增加了匹配的可行性，使我们能够利用预处理数据评估非混杂假设。对于统计推断，我们开发了一种标准化的安慰剂测试来解决不对称问题。通过数值模拟和空气污染预警实例对该方法进行了说明和评价。

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Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes

Abstract Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the unconfoundedness assumption using pre-treatment data. For statistical inference, we develop a normalised placebo test to address the asymmetry issue. The method is illustrated and evaluated on numerical simulations and a case study on air pollution alerts.

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来源期刊

Journal of the Royal Statistical Society Series B-Statistical Methodology 数学-统计学与概率论

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.