9月1日 | 姚方:Weak Separability Test for Spatial Functional Fields

時間:2020-08-30浏覽:71設置

  間:202091(周二)下午15:30-17:00

  點: 騰訊會議ID578 184 484

题  目:Weak Separability Test for Spatial Functional Fields

主講人:姚方  北京大學講席教授北大統計科學中心主任

摘  要:For analysis of spatial temporal data from a functional perspective, a heuristic extension of  Karhunen-Loeve expansion is often used to decompose such data into temporal components and spatially correlated random fields. This structure provides a convenient tool to investigate the space-time interactions, but may not always hold for complex situations. In this work, we introduce a new concept of weak separability, and propose formal testing procedures to examine the validity of the heuristic Karhunen-Loeve decomposition. Asymptotic properties are studied to avoid using resampling procedures, e.g. bootstrap. Both parametric and nonparametric approaches are developed to estimate the asymptotic covariance by constructing lagged type estimators. We demonstrate the efficacy of our method via simulations, and illustrate the usefulness using two real examples: Harvard forest data and China PM2.5 data.

報告人簡介:

北京大學講席教授北大統計科學中心主任。数理统计学会(IMSFellow,美國統計學會(ASAFellow2000年本科畢業于中國科技大學統計專業,2003獲得加利福尼亞大學戴維斯分校統計學博士學位,曾任職于多倫多大學統計科學系終身教授。現擔任Canadian Journal of Statistics的主編,至今擔任9個國際統計學核心期刊編委,包括統計學頂級期刊Journal of the American Statistical Association Annals of Statistics


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