WORKSHOP预告
时间：2026年6月26日（周五）15:00-18:00
地点：暨南大学石牌校区经济学院大楼（中惠楼）102
主持人：刘晓玉 暨南大学
时间 | 主题 | 专家 |
15:00-16:00 | Orthogonalized Score Tests for ConditionalVariable Significance in Deep Partial Linear Cox Models | 郝美玲 （对外经济贸易大学） |
16:00-17:00 | Inference for mark-specific causal effects | 曲连强 （华中师范大学） |
17:00-18:00 | Distributed privacy-preserving group inference forhigh-dimensional generalized linear models | 韩东啸 （南开大学） |
报告摘要及专家简介
郝美玲
郝美玲，对外经济贸易大学教授，香港理工大学博士，玛格丽特公主癌症研究中心博士后。主要研究领域：高维数据分析，生物统计，非参数统计，强化学习。主持国自然青年和面上基金项目，学术论文发表于Journal of the American Statistic Association, Journal of Machine Learning Research, Statistica Sinica, The Electronic Journal of Statistics, Computational Statistics & Data Analysis, Statistical Methods in Medical Research等期刊。
报告摘要
Conditional statistical inference for high-dimensional survival data remains a fundamental yet challenging problem, particularly when the effects of nuisance variables are complex and difficult to specify parametrically. In this paper, we propose a Deep Partial Linear Cox model that leverages neural networks to flexibly capture nonlinear nuisance effects while preserving interpretability for variables of primary interest. To test the conditional significance of high-dimensional variable sets within this framework, we develop an orthogonalized score test that effectively removes the influence of estimated nuisance components, thereby achieving valid inference in the presence of complex data dependencies. Our method accommodates settings where the number of tested parameters exceeds the sample size, without imposing sparsity assumptions. We establish the limiting null distribution of the proposed test statistic. Extensive numerical studies and an application to the TCGA breast cancer dataset demonstrate the superior performance and practical utility of our approach.
曲连强
曲连强现为华中师范大学副教授。主要研究方向为生存分析和复杂数据统计推断。现已发表学术论文18篇，包括JASA、Biometrika，JMLR 以及JBES等。
报告摘要
This paper presents a framework for causal inference in the presence of censored data,where the failure time is marked by a continuous variable referred to as a mark.The mark is observed after treatment and is not meaningful when the failure time is censored.In addition, due to the continuous nature of the marks, observations at each given mark are sparse. These facts make the identification and estimation of causality a challenging task.To address these issues, we define a new mark-specific treatment effect within the potential outcomes framework and characterize its identifying conditions. We then propose a local smoothing estimator for the causal effects and establish its asymptotic properties. We further develop testing methods to evaluate whether the treatment has an effect on the failure time when controlling the values of the mark at certain points or within a defined interval, and develop a Gaussian approximation method to obtain the critical values. We evaluate our method using simulation studies as well as a real dataset from the Antibody Mediated Prevention trials.
韩东啸
韩东啸，南开大学统计与数据科学学院，副教授，南开大学百青带头人。入选教育部青年人才计划。2011年获得南开大学学士学位，2016年获得中国科学院大学博士学位，主要研究方向为生存分析、高维统计推断、机器学习理论研究。在统计学国际顶尖期刊“JASA”、机器学习顶级期刊“JMLR”、计量经济学顶级期刊“JOE”等上发表论文十余篇。主持国家自然科学基金面上项目、国家自然科学基金青年项目。担任中国现场统计研究会贝叶斯统计分会首届常务理事、全国工业统计学教学研究会数字经济与区块链协会常务理事、全国工业统计学教学研究会青年统计学家协会第二届理事会理事、中国现场统计研究会资源与环境统计分会理事。
报告摘要
This article introduces a novel method for distributed, differentially private group inference in the high-dimensional generalized linear model. Each client first constructs a local non-private test statistic based on a weighted quadratic function of the regression sub-vector corresponding to the group, and the debiasing and re-weighting techniques. We then formulate our global differentially private test statistic by using the one-shot method, the Gaussian mechanism, and encryption and decryption procedures. Our proposed approach offers several key advantages: It is capable of handling highly correlated covariates and preserving high power foridentifying dense but weak signals. Unlike conventional methods, it avoids the need to handle the Hessian and precision matrices. Furthermore, the approach incorporates bounded encryption procedures to ensure secure communication and enable precise sensitivity quantification. Moreover, the one-shot aggregation mechanism in our method guarantees high efficiency in both computation and communication. Simulation studies are carried out to examine the finite-sample behaviour of the proposed method. An application to an adult income dataset is provided.
校对 | 刘晓玉
责编 | 彭毅
初审 | 姜云卢
终审发布 | 何凌云
（来源：暨南大学经济学院微信公众号）
