报告题目：Semi-supervised distribution learning

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.

报告时间：2024年11月26日16:00-17:00

报告地点：统计与数据科学学院109教室

专家简介：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“长江学者奖励计划”特聘教授，享受国务院政府特殊津贴专家。兼任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、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。获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。