报告人:  Yuehua Wu教授（ York University）

报告时间：2023年11月15日， 15:30-16:30

题目：A general latent dimension estimation method for nonstationary processes

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：吴月华，加拿大约克大学数学与统计系教授。1989年获得美国匹兹堡大学统计学博士学位，师从世界著名统计学家C. R. Rao。研究领域广泛，包括空间统计、M-估计、模型选择、变点检测、非参数估计、金融统计等，以及在环境科学、信息科学、计量经济学、生物医学等领域中的应用，目前是国际统计学会的当选会员。在Proceeding of National Academy Science, USA,（美国国家科学院院刊），Biometrika，Statistica Sinica， Computational Statistics & Data Analysis, Journal of Multivariate Analysis等期刊发表学术论文140多篇。承担加拿大国家自然科学基金、加拿大环境署等多项科研项目。

邀请人：徐礼柏