高维随机数R
High-Dimensional Metrics in R
摘要：
软件包High-dimensional Recognition（\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。它的重点是提供置信区间和显着性测试（可能有很多）的高维参数向量的低维子组件。目标变量上回归系数的有效估计量和一致有效置信区间（例如，治疗或策略变量），用于平均处理效应（ATE）和被治疗者的平均处理效应（ATET），以及用于将这些参数扩展到内源设置。基于理论，数据驱动的方法选择惩罚参数下异方差和非高斯误差的Lasso回归。此外，联合/同时置信区间的回归系数的高维稀疏回归，包括一个联合显著性检验的Lasso回归。数据集已被用于文献中，可能是有用的课堂演示和测试新的估计。\R和\Rpackage{hdm}软件包是开源软件项目，可以从CRAN免费下载：\texttt{http://cran.r-project.org}。
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
