发帖
雷达卡
摘要翻译：
考虑一个研究人员基于美国所有50个州的数据或对网站的所有访问数据来估计回归函数的参数。如何解释估计的参数和标准误差？在实践中，研究人员通常假设样本是从大量感兴趣的人群中随机抽取的，并报告旨在捕捉样本变化的标准错误。即使在很难明确表示感兴趣的群体是什么以及它与样本有何不同的应用程序中，这种情况也很常见。在本文中，我们探索了一种替代推理的方法，它部分是基于设计的。在基于设计的环境中，一些回归数的值可以被操纵，也许是通过政策干预。基于设计的不确定性源于对替代干预下回归结果的价值缺乏知识。我们推导出标准误差，它考虑了基于设计的不确定度，而不是基于抽样的不确定度，或者除了基于抽样的不确定度之外。我们证明了我们的标准误差一般比通常的基于无限总体抽样的标准误差小，并给出了它们重合的条件。
---
英文标题：
《Sampling-based vs. Design-based Uncertainty in Regression Analysis》
---
作者：
Alberto Abadie, Susan Athey, Guido W. Imbens, and Jeffrey M.
Wooldridge
---
最新提交年份：
2019
---
分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--
---
英文摘要：
Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors? In practice, researchers typically assume that the sample is randomly drawn from a large population of interest and report standard errors that are designed to capture sampling variation. This is common even in applications where it is difficult to articulate what that population of interest is, and how it differs from the sample. In this article, we explore an alternative approach to inference, which is partly design-based. In a design-based setting, the values of some of the regressors can be manipulated, perhaps through a policy intervention. Design-based uncertainty emanates from lack of knowledge about the values that the regression outcome would have taken under alternative interventions. We derive standard errors that account for design-based uncertainty instead of, or in addition to, sampling-based uncertainty. We show that our standard errors in general are smaller than the usual infinite-population sampling-based standard errors and provide conditions under which they coincide.
---
PDF链接：
https://arxiv.org/pdf/1706.01778
京公网安备 11010802022788号
