A Sequential Learning Procedure for Estimating Coefficients in Linear Regression With Applications to Online Sales Examination

ABSTRACT In this paper, we consider the problem of estimating coefficients 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.