【学术讲座】FROM MODEL SELECTION TO MODEL AVERAGING: A COMPARISON FOR NESTED LINEAR MODELS

【专家简介】 张新雨，中科院数学与系统科学研究院预测中心研究员。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括模型平均、机器学习和组合预测等。担任期刊《JSSC》领域主编、期刊《系统科学与数学》、《数理统计与管理》等的编委，是双法学会数据科学分会副理事长、国际统计学会当选会员，先后主持自科优秀和杰出青年基金项目。

【报告摘要】 Model selection (MS) and model averaging (MA) are two popular approaches when having many candidate models. Theoretically, the estimation risk of an oracle MA is not larger than that of an oracle MS because the former one is more flexible, but a foundational issue is: does MA offer a substantial improvement over MS? Recently, a seminal work: Peng and Yang (2021), has answered this question under nested models with linear orthonormal series expansion. In the current paper, we further reply this question under linear nested regression models. Especially, a more general nested framework, heteroscedastic and autocorrelated random errors, and sparse coefficients are allowed in the current paper, which is more common in practice. In addition, we further compare MAs with different weight sets. Simulation studies support the theoretical findings in a variety of settings.(Jointly with Dr. Wenchao Xu).

地  点：位育楼417

时  间：2022年9月3日15：00-17:00