We provide finite sample bounds on the Normal approximation to the law of the least squares estimator of the projection parameters normalized by the sandwich-based standard errors. Our results hold in the increasing dimension setting and under minimal assumptions on the data generating distribution. In particular, we do not assume a linear regression function and only require the existence of finitely many moments for the response and the covariates. Furthermore, we construct confidence sets for the projection parameters in the form of hyper-rectangles and establish finite sample bounds on their coverage and accuracy. We derive analogous results for partial correlations among the entries of sub-Gaussian vectors. \end{abstract}

翻译：我们提供与以三明治为基础的标准差使预测参数正常化的最小方形估计参数定律的正常近似值的有限样本界限。 我们的结果维持在不断增长的维度设置和数据生成分布的最低假设之下。 特别是, 我们不承担线性回归函数, 只需要响应和共变量存在有限的许多时刻。 此外, 我们以超矩形的形式为预测参数建立信任套件, 并设定其覆盖范围和准确性的有限样本界限。 我们得出类似的结果, 显示亚高加索矢量条目之间的部分相关性 。\ end{ abstract}