信息来源：中国人民大学健康大数据研究院 发布日期：2026年7月13日

在小说阅读器读本章去阅读在小说阅读器中沉浸阅读

为进一步加强学术交流合作，营造浓厚学术氛围，推动科研灵感互鉴，激发科研创新活力，健康大数据研究院拟面向全校师生组织开展学术沙龙活动，欢迎各位老师同学踊跃报名参加，谢谢！

活动时间

2026年7月20日（周一）9:00

活动地点

中国人民大学中关村校区（具体地点将在报名后于群内通知）

沙龙主题

Modern Statistical Methods in the Era of AI

邀请嘉宾（按姓氏字母排序）

01 Willard John Braun

个人简介：Dr. W. John Braun was Professor of Statistics at the University of Western Ontario before joining UBC's Okanagan campus as Head of Computer Science, Mathematics, Statistics and Physics. He served as Deputy Director of the Canadian Statistical Sciences Institute (CANSSI) for five years, and he has supervised over 50 graduate students. Braun has co-authored three books that feature the R programming language as well as 80-plus papers on nonparametric statistics and stochastic modelling, often motivated by scientific problems.

报告题目：Iterated Data Sharpening in Local Linear Regression

报告摘要：Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure have been less effective, due to the employment of an inappropriate sharpening transformation. In the present paper, we propose an iterated data sharpening algorithm which reduces the asymptotic bias at each iteration. The efficacy of the iterative approach is demonstrated theoretically and via a simulation study. Boundary bias effects are also explored. Simulations also show that after iteration, the resulting kernel regressions are less sensitive to bandwidth choice, and a further simulation study demonstrates that iterated data sharpening with data-driven bandwidth selection via cross-validation can lead to more accurate regression function estimation. Examples with real data are used to illustrate the scope of change made possible by using iterated data sharpening.

02 Marcelo Bourguignon

个人简介：Marcelo Bourguignon is an associate professor at the Federal University of Rio Grande do Norte, located in Natal (Brazil). He carried out his doctoral studies (2011‑2014) at the Federal University of Pernambuco, Recife, Brazil. His studies were related to count time series, survival analysis, distribution theory, and regression models. He has 130 published papers.

报告题目：A general and unified parameterization of the beta distribution: A flexible and robust beta regression model

报告摘要：The beta regression model is a commonly used approach for modeling data in the unit intervals, such as rates, ratios, percentages, or proportions. The usual mean beta regression model provides the average relationship between a response variable and covariates. However, there are limitations of the conditional mean models, leading, in some cases, to wrong conclusions or, at best, inappropriate statistics. In this paper, we extend the usual mean beta regression model using a general and unified parameterization of this distribution that is indexed by some central tendency measure, such as median, mode, arithmetic mean, geometric mean or harmonic mean, and a concentration parameter. In this new regression model, the central tendency measure response is related to a linear predictor through a link function and the linear predictor involves covariates and unknown regression parameters. This approach is naturally robust in the presence of outliers. We also propose a simple interpretation of the predictor response relationship in terms of the percentage increases or decreases in the logarithm of the odds ratio of some central tendency measure of the response. The maximum likelihood method is used for estimating the model parameters. A Monte Carlo experiment is conducted to evaluate the performance of these estimators and residuals in finite samples on the influence of outliers by considering contaminated data under a perturbation scheme to generate outliers were carried out and confirm that the proposed regression model seems to be a new robust alternative for modeling continuous data limited to the unit interval. The usefulness of the new regression model is illustrated through two real applications.

03 Lengyi Han

个人简介：Dr. Lengyi Han received her Ph.D in Statistics at the University of Western Ontario. Her thesis concerned the statistical modelling of wildfire. She worked as a statistical consultant, focussing on health and environmental modelling problems. She joined the Department of Computer Science, Mathematics, Physics and Statistics at UBC Okanagan as a faculty member in 2020. She is a teaching stream professor with a focus on applied statistics as well as pedagogical research.

报告题目：Seasonal Minification Processes

报告摘要：The minification model is a discrete time, continuous state Markov process which can be applied to nonnegative time series. In this talk, we show how seasonality can be modelled with minification processes. Stationarity, moments and autocorrelations are studied for such processes. Parameter estimation can be carried out using method of moments and generalized method of moments techniques. An application to Fire Weather Index data from Northwestern Ontario is presented, including a comparison between the nonseasonal and seasonal models.

04 Paulo Jorge Canas Rodrigues

个人简介：Paulo Canas Rodrigues is a Professor of Statistics and Data Science at the Federal University of Bahia and the Director of the Statistical Learning Laboratory (SaLLy; www.SaLLy.ufba.br). Paulo completed his Ph.D. in Statistics at the Nova University of Lisbon, Portugal (2012), and his Habilitation in Mathematics, with a specialization in Statistics and Stochastic Processes, at the Lisbon University, Portugal (2019). His research in time series forecasting, statistical learning, artificial intelligence, statistics, and data science resulted in more than 130 scientific papers in collaboration with more than 200 co‑authors from 95 universities in 31 countries and delivered more than 200 invited talks at conferences and scientific seminars. He is an Elected Member of the International Statistical Institute. Among other activities, he is the current President of the International Association for Statistical Computing, the Past‑President of the International Society for Business and Industrial Statistics, a Member of the Representative Council of the International Biometric Society, and a Council Member of the International Statistical Institute. Website: www.paulocanas.org; www.SaLLy.ufba.br.
报告题目：Time series forecasting: Exploring hybrid strategies with singular spectrum analysis

报告摘要：Time series forecasting plays a key role in areas such as energy, environment, economy, and finances. Hybrid methodologies, combining the results of statistical, mathematical, and machine learning methods, have become popular for time series analysis and forecasting, as they allow researchers to compensate for the limitations of one approach with the strengths of the other and combine them into new frameworks while improving forecasting accuracy. In this class of methods, algorithms for time series forecasting are applied sequentially, i.e., the second algorithm is applied to the residuals that were not captured by the first one. In this talk, I will discuss several hybrid strategies for time series forecasting that use singular spectrum analysis, classical time series models, and recurrent neural networks, with application to several areas of research.

报名方式

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中国人民大学健康大数据研究院于2023年5月获批成立，为学校首批建设的16个创新高地之一。研究院聚焦国民健康中的数据科学挑战，面向国民健康测度、评估、管理等社会重大现实问题开展基础研究与应用研究，致力于服务“健康中国”国家战略重大需求、构建健康大数据领域国内领先、国际一流的创新型研究高地。