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摘要翻译：
由于高维经验在许多研究领域的应用越来越广泛，有效的同时推理变得越来越重要。例如，在经济研究中，由于具有许多潜在协变量的非常丰富的数据集或在治疗异质性分析中，可能会出现高维设置。此外，对回归关系的潜在更复杂（非线性）函数形式的评估导致了许多潜在的变量，对这些变量，同时推理语句可能是感兴趣的。在这里，我们回顾了在（高维）环境中同时推理的经典和现代方法，并通过一个使用R包HDM的案例研究来说明它们的使用。R包hdm实现了有效的联合、强大和高效的假设检验，以及同时构造置信区间，因此，提供了有效的基于LASSO的选择后推理的方法。
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英文标题：
《Valid Simultaneous Inference in High-Dimensional Settings (with the hdm
package for R)》
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作者：
Philipp Bach, Victor Chernozhukov, Martin Spindler
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最新提交年份：
2018
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分类信息：
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。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and efficient hypothesis tests for a potentially large number of coeffcients as well as the construction of simultaneous confidence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO.
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PDF链接：
https://arxiv.org/pdf/1809.04951
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