报告人: 王占锋副教授（中国科学技术大学）

报告时间：2023年11月18日，15:20-16:10

报告地点：金融工程研究中心105报告厅

报告题目：Estimation and model selection for nonparametric function-on-function regression

报告摘要：Regression models with a functional response and functional covariate have received significant attention recently. While various nonparametric and semiparametric models have been developed, there is an urgent need for model selection and diagnostic methods. In this article, we develop a unified framework for estimation and model selection in nonparametric function-on-function regression. We propose a general nonparametric functional regression model with the model space constructed through smoothing spline analysis of variance (SS ANOVA). The proposed model reduces to some of the existing models when selected components in the SS ANOVA decomposition are eliminated. We propose new estimation procedures under either L1 or L2 penalty and show that the combination of the SS ANOVA decomposition and L1 penalty provides powerful tools for model selection and diagnostics. We establish consistency and convergence rates for estimates of the regression function and each component in its decomposition under  both the L1 and L2 penalties. Simulation studies and real examples show that the proposed methods perform well. Technical details and additional simulation results are available in online supplementary materials.

个人简介：王占锋，中国科学技术大学统计与金融系副教授，应用统计专业硕士学位项目主管。分别于2003年和2008年获中国科学技术大学学士和理学博士学位。主要从事统计渐近理论，生物统计，函数型数据分析等领域的研究，在国内外学术期刊上发表论文40多篇。曾主持国家自然科学青年基金和面上基金各一项，参与国家重点自然科学基金两项。全国工业统计学研究会数字经济与区块链技术协会秘书长，中国现场统计研究会资源与环境统计分会常务理事，中国现场统计研究会旅游大数据学会常务理事。

邀请人：徐礼柏