R中的高维度量

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摘要翻译：
包高维度量(\rpackage{hdm}）是一个不断发展的统计方法集合，用于估计和量化高维近似稀疏模型中的不确定性。它侧重于为高维参数向量的（可能很多）低维子分量提供置信区间和显著性检验。提供了高维近似稀疏回归模型中目标变量（例如，治疗或策略变量）的回归系数、平均治疗效果(ATE)和被治疗者的平均治疗效果(ATET)以及这些参数对内生设置的扩展的有效估计量和一致有效置信区间。在异方差和非高斯误差情况下，实现了基于理论的、数据驱动的Lasso回归惩罚参数选择方法。此外，实现了高维稀疏回归的回归系数的联合/同时置信区间，包括Lasso回归的联合显著性检验。文献中使用的数据集可能对课堂演示和测试新的估计量有用。\r和包\rpackage{hdm}是开源软件项目，可以从cran:\texttt{http://cran.r-project.org}免费下载。
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英文标题：
《High-Dimensional Metrics in R》
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作者：
Victor Chernozhukov and Chris Hansen and Martin Spindler
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最新提交年份：
2016
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分类信息：

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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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：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--

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英文摘要：
  The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and average treatment effect for the treated (ATET), as well for extensions of these parameters to the endogenous setting are provided. Theory grounded, data-driven methods for selecting the penalization parameter in Lasso regressions under heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression coefficients of a high-dimensional sparse regression are implemented, including a joint significance test for Lasso regression. Data sets which have been used in the literature and might be useful for classroom demonstration and for testing new estimators are included. \R and the package \Rpackage{hdm} are open-source software projects and can be freely downloaded from CRAN: \texttt{http://cran.r-project.org}.
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PDF链接：
https://arxiv.org/pdf/1603.01700

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关键词：econometrics coefficients significance Multivariate SIMULTANEOUS cran 变量 Metrics dimensional hdm