In this paper, we consider the problem of estimating parameters in a linear regression model. We propose a sequential learning procedure to determine the sample size for achieving a given small estimation risk, under the widely used Gauss-Markov setup with independent normal errors. The procedure is proven to enjoy the second-order efficiency and risk-efficiency properties, which are validated through Monte Carlo simulation studies. Using e-commerce data, we implement the procedure to examine the influential factors of online sales.

翻译：本文研究线性回归模型中的参数估计问题。在经典的高斯-马尔可夫设定下（假设独立正态误差），我们提出了一种序贯学习过程，用于确定达到给定小估计风险所需的样本量。该过程被证明具有二阶效率与风险效率性质，并通过蒙特卡洛模拟研究得到验证。基于电子商务数据，我们应用该过程检验在线销售的影响因素。