讲座人简介：

虞克明（Keming Yu），英国伦敦布鲁内尔大学（Brunel University of London）统计学与数据科学首席教授（Chair Professor）、布鲁内尔大学数学学科研究影像中心主任，以及统计与数据分析硕士研究生课程主任、英国皇家统计协会会士。

虞克明教授已在Journal of the American Statistical Association、Journal of the Royal Statistical Society、Journal of Econometrics、 Journal of Business & Economics Statistics、 Statistica Sinica等国际学术期刊发表论文160余篇。自2019年美国斯坦福大学首次发布全球前2%顶尖科学家排行榜（World's Top 2% Scientifics）至今，虞克明教授一直在榜多年。

虞克明教授已受邀担任Journal of the American Statistical Association、A&CS、The Royal Statistical Society-A、 The Royal Statistical Society-C、Statistical and Its Interface、Journal of Statistical Theory and Practice Review等国际期刊的副主编（Associate Editor），受邀担任欧盟科学基金、英国自然科学基金、英国社会科学基金上会评审专家。

讲座简介：

Regression models beyond mean, such as quantile regression and expectile regression, are extremely useful in many real-world applications where effects of explanatory variables vary across different outcome levels (e.g., income, health, risk) and the extreme cases, where the mean regression (like ordinary least squares, OLS) is too limited. Coherent risk measurers are crucial in finance and risk management because they provide a mathematically sound and economically meaningful way to assess the risk of financial positions and overcome key limitations of older measures like Value-at-Risk (VaR) and ensure consistency in decision-making. Our work makes a novel contribution by introducing a novel family of regression models beyond mean, namely Generalised Quantiel Regression (GQR), alongside coherent risk measurers. Many traditional regression models and risk measures can be viewed as special cases of GQR. As a flexible non-parametric regression model, GQR demonstrates outstanding performance in handling high-dimensional and large datasets, particularly those generated by distributed systems, offering a convenient framework for their statistical analysis. We derive the corresponding estimators and develop their asymptotic properties. Simulations and real data analyses are conducted to illustrate the finite-sample performance of the proposed methods.