报告人：Yuehua Wu教授， 加拿大约克大学

报告时间：2023.11.20（星期一），14:30-15:30

报告地点：数学楼2-2

报告题目：A general latent dimension estimation method for nonstationary processes

报告摘要：In this talk, we consider the problem of modeling nonstationary processes. Bornn et al. (2012) proposed a dimension expansion method, a novel technique for modeling nonstationary processes, aiming to find a dimensionally sparse projection in which the originally nonstationary field exhibits stationarity. However, their dimension expansion approach is a lasso-penalized least-squares method that does not account for the covariance structure of the empirical semivariogram. We thus propose a general latent dimension estimation method by replacing the least-squares method with generalized least-squares (GLS). Furthermore, we improve the GLS method by weighted least-squares, which is more computationally efficient and accurate. The performance of the proposed methods is demonstrated through simulations and real data examples.

报告人简介：Yuehua Wu，加拿大约克大学统计系教授，1989年获美国匹兹堡大学的统计学博士学位，师从世界著名统计学家C. Rao。目前，从事高维数据分析、模型选择、变化点分析、时空建模、环境统计和统计金融等多领域研究。在《Proceedings of the National Academy of Sciences》、《Biometrika》、《Statistica Sinica》、《Econometrics》、《Computational Statistics & Data Analysis》、《Statistics Computing》、《Journal of Multivariate Analysis》、《Science China》等高影响力期刊上发表了135余篇学术论文，出版了7本专著。主持加拿大自然科学基金高维复杂数据的统计建模与推断科研项目。