为更好地推动统计学、数据科学及相关学科的发展，促进国内青年统计学者之间的学术交流与合作，全国工业统计学教学研究会青年统计学家协会2026年年会将于2026年4月11日在西南财经大学柳林校区（成都）举办。此次会议由全国工业统计学教学研究会青年统计学家协会主办，西南财经大学统计与数据科学学院、西南财经大学统计交叉创新研究院、西南财经大学数据科学与商业智能联合实验室承办，《统计理论及其应用（英文）》编辑部、狗熊会协办。论坛邀请国内外知名统计学家和杰出青年统计学者做大会报告，并邀请国内优秀青年统计学者到会开展深入探讨，也为有志于进入高校发展的统计学人才以及有意求贤的高校，提供互相展示、沟通和了解的交流平台（点击阅读原文下载会议通知）。
邀请报告｜复杂时间序列分析
报告题目
Structural Break-driven Optimal Subsample Forecast Combination for Factor-augmented Regressions
报告人简介
汪思韦, 湖南大学
汪思韦，湖南大学金融与统计学院副教授。主要从事非参数计量建模、高维数据分析等领域研究。目前在 Journal of Econometrics, Oxford Bulletin of Economics and Statistics，Economics Letters以及《系统工程理论与实践》等计量经济学专业期刊上发表学术论文。现主持国家自然科学基金青年科学基金项目以及湖南省自然科学基金青年科学基金项目。
报告摘要
In the practice of economic and financial time series forecasting in data-rich environment, structural breaks are pervasive, and while integrating pre- and post-break data has long been recognized to potentially enhance prediction accuracy compared to relying solely on post-break information, a consensus on effectively leveraging break information remains elusive. This paper addresses this gap by proposing a novel subsample forecast combination scheme for factor-augmented regressions: subsamples are constructed based on the identified most recent break, with a subsample tuning parameter governing subsample specifications (length and quantity), candidate forecasts are generated from factor-augmented regressions within each subsample to summarize break-related information (e.g., magnitude, location), and forecast combinations are derived via weights that minimize a forward validation criterion alongside optimal subsample specification selection. Theoretical analysis establishes uniform consistency of estimated coefficients and asymptotic optimality of selected weights and subsample specifications; further, if correctly-specified models exist among candidate subsample forecasts, they are assigned all weights with probability approaching one. Numerical results from simulations and a real-data application to macroeconomic forecasting demonstrate the combination strategy's superior practical performance.
报告题目
Generative Doubly Robust Estimation for General Treatment Effects
报告人简介
钟齐先, 厦门大学
钟齐先，厦门大学经济学院副教授，主要研究领域为生存数据、深度学习、函数型数据分析和因果推断等，其学术成果发表在AOS、Biometrika、JASA、JBES、JOE、NeurIPS等学术期刊或会议上，主持国家自然科学基金青年和面上项目各一项和主要参与一项国家重点研发计划青年科学家项目。
报告摘要
This paper introduces a unified framework for doubly robust (DR) estimation of a broad class of causal functionals, including average, quantile, and asymmetric least squared treatment effects, as well as their conditional counterparts. While DR estimators are well-established for average treatment effects, their development for distributional parameters like quantile treatment effects has not yet been investigated. We bridge this gap by integrating conditional generative models into a loss-based estimating framework. Our approach uses generative models to synthesize counterfactual samples, defines a target loss whose minimizer corresponds to the causal functional of interest, and constructs a final DR estimator by combining these elements with inverse probability weighting. The resulting estimators are shown to be root-$n$ consistent, asymptotically normal, and semiparametrically efficient for unconditional effects, provided either the propensity score or the generative model is correctly specified. For conditional effects, we employ deep neural networks, establishing minimax-optimal convergence rates that adapt to low intrinsic data structures. Simulations confirm the double robustness and finite-sample performance of the proposed methods. This work provides a robust and flexible tool for distributional and heterogeneous causal inference in observational studies, where model misspecification is a persistent concern.
报告题目
Estimation of Change Points in High-Dimensional Constrained Factor Models with Small T
报告人简介
向镜洁，华中师范大学
向镜洁，华中师范大学经济与工商管理学院副教授，研究方向包括高维因子模型、交互效应面板模型的理论与应用，以第一作者或通讯作者身份在《Econometric Reviews》、《Oxford Bulletin of Economics and Statistics》、《Economics Letters》、《Pacific-Basin Finance Journal》、《Emerging Markets Review》、《金融研究》等期刊发表论文多篇，主持国家自然科学基金青年项目1项。
报告摘要
This paper considers the estimation of break points in high-dimensional constrained factor models where the number of time periods (T) is much smaller than the number of cross sections (N). We establish the conditions under which the least squares (LS) estimator is consistent for both large and small breaks. For large breaks, the estimator is also consistent under fixed T. If the number of factors is unobservable, we further show the consistency of the LS estimator based on the estimated number of pseudo factors minus one. Simulation results confirm that the break date can be accurately estimated when T is small. In empirical applications, the method is implemented to estimate the break date in global firm-level carbon emissions from 2010–2019. We find that the structural break occurs in 2015, which coincides with the establishment of the Paris Agreement.
报告题目
Deep Nonlinear Factor Dimension Reduction for Multiple Time Series via GMDD
报告人简介
戴爽，中国科学院
戴爽，中国科学院数学与系统科学研究院博士后，2024年博士毕业于华东师范大学统计学院。主要研究方向为高维数据分析与时间序列分析，相关研究成果发表于Statistica Sinica、Statistics and Computing以及Journal of Nonparametric Statistics等学术期刊。
报告摘要
We propose a novel framework for factor dimension reduction in vector time series by extending the deep nonlinear sufficient dimension reduction method (GMDD-Net; Chen et al., 2024) to strictly stationary processes. The proposed approach aims to construct a contemporaneous nonlinear transformation of a p-dimensional time series into a small number of lower-dimensional subseries, thereby effectively capturing the underlying nonlinear factor structures. From a theoretical perspective, we show that the proposed one-step estimation procedure with squared Frobenius norm regularization is unbiased at the general σ-field level. To establish non-asymptotic convergence rates, we recursively apply the Coupling Lemma of Rio (2017) to construct a sequence of independent blocks of length q0, which allows us to rigorously control the approximation error induced by replacing the initial β-mixing sequence with its coupled version. The resulting convergence rates are slower than those obtained in the i.i.d. case by a factor of (log n)1r for r1 under exponential decay, and by n1r for r>2 under polynomial decay, reflecting the impact of temporal dependence in multivariate time series. Numerical experiments on both simulated and real datasets indicate that the proposed method effectively reduces dimensionality while preserving essential nonlinear structures, facilitating the modeling and forecasting of high-dimensional nonlinear dynamical systems.
博士生论坛
此次会议设有超过10场博士生论坛，欢迎在读博士生投稿（投稿要求会在稍后专门发布推文）。一旦入选将有机会在博士生论坛进行宣讲，并且得到travel award。成功宣讲的博士生会得到协会颁发的宣讲证书。
高校招聘专场
为了更好地促进高校与博士生之间的交流，协会特设高校招聘专场，费用6000元/个。2024年和2025年的年会成功吸引了超过30家高校和企业到现场进行招募。有意向的高校请将基本情况发送邮件到 feng.li@gsm.pku.edu.cn，与李老师联络。请在邮件中说明学校或学院的基本情况，联系人方式等。
会议举办地点
西南财经大学柳林校区 四川成都温江柳台大道555号
联系人：潘蕊 panrui_cufe@126.com
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