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                    尤進紅教授學術講座

                    2023年12月01日 09:19  點擊:[]


                    講座題目: Periodicity Learning for Non-stationary Functional Time Series

                    主 講 人: 尤進紅

                    時    間: 2023年2023年12月7日14:30-15:30

                    地    點: 西安交通大學創新港涵英樓經濟金融研究院8121會議室

                     

                    報告人簡介:

                    尤進紅教授, 加拿大女皇大學(University of Regina)統計學博士,美國北卡羅納教堂山分校博士后, 上海財經大學統計與管理學院tenured常任軌教授、博士生導師和統計與數據科學研究院副院長,全國工業統計學教學研究會第九屆理事會副會長,全國統計學交叉學科分會副會長,曾任Quality Technology and Quantitative Management (QTQM), special issue: Mathematical and Statistical Finance的客座編委(Guest Editor)。 尤進紅教授有十余年的北美學習、工作經歷。長期從事計量經濟學、數理統計以及生物統計的科學研究; 在面板數據、函數型數據數據分析及其在經濟學, 金融學和生物醫學方面的應用開展了許多有價值的研究工作;在國際和國內著名的統計學、經濟學和數據科學雜志(包括Journal of the American Statistical Association,Journal of Econometric,Journal of Machine Learning Research等)上發表學術論文八十余篇,其中三大檢索論文六十余篇,被SCI他引幾百余次;主持和參與了多個國家自科基金項目。為國際著名統計和計量經濟學雜志Annals of Statistics, Journal of the American Statistical Association , Biometrika和Journal of Econometric等的論文評審人。曾獲上海市教委教學成果一等獎,上海財經大學先進工作者稱號等獎項。

                    Abstract:

                    Existing literature on non-stationary functional time series primarily focuses on testing for stationarity and the existence of periodicity or trends. In this article, we present the first attempt to investigate the non-stationary periodic functional time series that has an unknown period and a functional trend. To achieve this, we propose a nonparametric profile estimation method that accurately estimates the unknown period, periodic component, and trend function. We establish the asymptotic properties of the estimators, including period estimate consistency and the asymptotic behaviours of the estimated periodic component and trend function. The finite sample performance of our method is investigated through simulation studies. Additionally, we apply our method to three real data applications, which include the functional time series of global temperature anomaly curves, daily CO and NOX concentrations in Hong Kong, and annual sunspot numbers. We report several intriguing discoveries. For instance, the estimated period of the functional time series of global temperature anomaly curves is 46 years, which is much shorter than the previously estimated period of 60 years based on conventional time series analysis.

                     

                     

                    西安交通大學經濟與金融學院

                    2023年11月24日

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