时 间：2025年5月9日 15:00-16:00
地 点：普陀校区理科大楼A1514
报告人：蒋学军 南方科技大学长聘副教授
主持人：唐炎林 华东师范大学教授
摘 要：
This research introduces an innovative inference framework that employs dimension reduced convolution-smoothed quantile regression, while avoiding estimating the inverse of high-dimensional covariance matrix of the predictors. By calibrating the regularization parameter, we develop a data-driven test that can be shown to be an oracle test with probability tending to one. To mitigate the selective bias induced by dimension reduction and ensure valid inference, we implement a cross-fitting strategy by dividing the dataset into two parts: one for model selection and the other for parameter estimation. This process yields a fused estimator, derived from an informative weighting method that combines estimators from both dataset partitions. The fused estimator aids in constructing confidence intervals and performing Wald-type tests for targeted parameters. We establish the Bahadur representation of this estimator and obtain limiting distributions of the test statistics under both null and alternative hypotheses, with the number of parameters diverging to infinity. Advantages of our tests are further highlighted by theoretical power comparisons to some competitive tests. Empirical studies confirm effectiveness of the proposed tests across various linear parameter hypotheses. Additionally, we illustrate the use of the proposed methodology through two real-world data analyses.
报告人简介：
南方科技大学统计与数据科学系长聘副教授、研究员、博士生导师，于2009年博士毕业于香港中文大学统计学系，2009-2010在港中文从事博士后研究，2010-2013任职中南财经政法大学，2013年07月加入南方科技大学，曾获南方科技大学杰出教学奖，深圳市优秀教师等荣誉，主持和完成国家（广东省）自然科学基金、深圳市基础研究面上项目等10余项。蒋老师研究兴趣涉及分位数回归、变量选择、假设检验、高维统计推断，金融统计与计量，迁移学习等，已在Biometrika, Bernoulli, Statistics and Computing, Statistica Sinica, Econometrics Journal, Science China-Mathematics，Financial Innovation等国内外统计学及计量经济学一流期刊上发表SCI&SSCI论文近60篇，发明专利2项及出版英文教材一部，目前担任Statistics and Its Interface副主编、中国现场统计研究会-教育统计分会副理事长及多元分析分会秘书长。