报告题目：Semi-supervised distribution learning

报告人：王兆军 教授 南开大学

报告时间：2025年5月12日 19:00-20:00

报告地点：腾讯会议ID：837-506-136

校内联系人：杜明月 mingydu@liuliantv.org

报告摘要：This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabeled data by approximating a conditional distribution on covariates. The proposed estimator is derived from the K-fold cross-fitting strategy, exhibiting both consistency and asymptotic Gaussian process properties. Under mild conditions, the proposed estimator outperforms the empirical cumulative distribution function in terms of asymptotic efficiency. Several applications of the methodology are given, including parameter inference and goodness-of-fit tests.

报告人简介：王兆军，南开大学统计与数据科学学院执行院长/教授，教育部长江学者特聘教授，国务院学位委员会统计学科评议组成员，全国统计教材编审委员会委员；中国工业与应用数学学会副理事长，中国统计教育学会副会长，中国工业统计教学研究会副会长，中国概率统计学会副理事长。曾任国家统计专家咨询委员会委员、国家自然科学基金委天元基金领导小组成员、中国现场统计研究会副理事长、天津市现场统计研究会理事长，天津工业与应用数学学会理事长，曾获国务院政府特贴、全国百篇优博指导教师、教育部自然科学二等奖及天津市自然科学一等奖。