A Goodness-of-fit Assessment for General Learning Procedure in High Dimensions

：朱利平（中国人民大学）
：2024-11-07 16:30
：海韵园行政楼C503

报告人：朱利平（中国人民大学）

时  间：2024年11月7日16:30

地  点：海韵园行政楼C503

内容摘要:

Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from classical linear regression to advanced neural networks. Our goodness-of-fit test leverages data-splitting, utilizing the test set to evaluate the black-box learner trained on the training set. This evaluation is based on examining the cumulative covariance of the residuals. Extensive simulations and two real data analyses validate the effectiveness of our method. 

个人简介：

朱利平，中国人民大学“杰出学者”特聘教授、博士生导师，统计与大数据研究院院长，国家重大人才工程入选者，国家杰出青年基金获得者。朱利平教授长期从事复杂数据分析方法和理论研究工作，在复杂高维、超高维数据领域以及非线性相依数据领域做出了一系列有影响力的研究工作。多篇论文入选ESI高被引论文。现任中国现场统计学会高维数据分会和生存分析分会副理事长，以及多个学会的常务理事、理事等。先后担任统计学领域国际顶级学术期刊《The Annals of Statistics》、国际重要学术期刊《Statistica Sinica》和《Journal of Multivariate Analysis》等的Associate Editor，以及《系统科学与数学》和《应用概率统计》等国内重要学术期刊编委。

 

联系人：周达