时间：11月10日13:00

地点：15号楼518会议室

主持人：赵岩斌

报告简介: 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.

主讲人简介：虞克明，浙江工商大学特聘教授，《Journal of the Royal Statistical Society-C》编委，曾担任《Journal of the American Statistical Association, A&CS》、《Journal of the Royal Statistical Society-A》等多家国际SCI、SSCI期刊的编委。2024年，被ScholarGPS评为高排名学者。虞教授是国际公认的贝叶斯分位数回归先驱，在统计方法论与数据科学领域具有深远影响。先后在《Journal of American Statistical Association》、《Journal of the Royal Statistical Society: Series B》、《Journal of Econometrics》、《Journal of Business & Economic Statistics》、《Bernoulli》等统计学顶级刊物上发表论文150多篇。