线性回归系数估计的序贯学习方法及其在在线销售考核中的应用

IF 1.3 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2024-11-13 DOI:10.1002/asmb.2903

Jun Hu, Yan Zhuang, Shunan Zhao

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引用次数: 0

摘要

本文研究线性回归模型中系数的估计问题。在具有独立正态误差的广泛使用的高斯-马尔可夫设置下，我们提出了一个顺序学习过程来确定实现给定小估计风险的样本量。该方法具有二阶效率和风险效率，并通过蒙特卡罗仿真研究进行了验证。利用电子商务数据，对影响网络销售的因素进行分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.