报告摘要：
We study the data-driven newsvendor problem in situations where historical demand data and associated feature information are available. To address this problem, we leverage information from similar data sources and employ transfer learning to improve decision-making and facilitate statistical inference on the parameter of primary interest in the target domain. Within the framework of a semiparametric regression model, we assume the existence of a shared feature representation for confounding effects across different tasks. By learning this shared representation from various source domains, we can effectively transfer it to the target domain. This approach enables both accurate decision-making and interpretability in the target domain. We establish sufficient conditions for model identifiability, derive a finite sample performance bound for the cost function, and prove that the estimator of the parameter of primary interest in the target model is consistent and asymptotically normal. Through simulation studies, we demonstrate the superiority of our method, and we further illustrate its practical applications in inventory decision-making forbike-sharing systems.
嘉宾简介：
张新雨，中科院数学与系统科学研究院研究员。主要从事计量经济学和统计学理论和应用研究工作，具体研究方向包括模型平均方法及其在经济预测、管理统计、机器学习和生物医学等领域的交叉研究。担任期刊SCI期刊《JSSC》领域主编、《系统科学与数学》等多个期刊的编委，曾获中国青年科技奖，先后主持国家自然科学基金C、B、A和A延续项目。
