题目：Nonparametric estimators of nonstationary densities of streaming data

主讲人：澳大利亚墨尔本大学数学与统计学院 Aurore Delaigle教授

主持人：西南财经大学统计与数据科学学院 常晋源教授

时间：2026年7月6日（周一）下午14:00-15:00

地点：西南财经大学光华校区光华楼1003会议室

报告摘要：

We consider nonparametric estimation of the nonstationary density of streaming data collected continuously over time. Those data are typically not entirely accessible at all times, and analyzing them requires dynamic approaches that do not require repeated access to past data. Several nonparametric estimators of nonstationary densities have been suggested in the literature, which all require choosing important tuning parameters at each time. We study theoretical properties of those estimators and propose a data-driven dynamic selection of their tuning parameters, which can be implemented iteratively and requires only sequential access to consecutive blocks of the most recent data, and which includes a selection of the sizes of the blocks. We illustrate the procedure through simulated and real streaming data.

主讲人简介：

Aurore Delaigle, Fellow of the Australian Academy of Science, is a Professor and ARC Future Fellow in the Department of Mathematics and Statistics at the University of Melbourne, Australia. Her research interests include nonparametric statistics, deconvolution and functional data analysis. Following her undergraduate degree in mathematics at Université catholique de Louvain, Belgium, she completed a PhD in statistics at the same institution on kernel estimation in deconvolution problems. In her early career, she undertook a postdoctoral fellowship at University of California, Davis, before joining University of California, San Diego as an assistant professor. She was also a Reader at the University of Bristol. In 2014, she was promoted to Professor at the University of Melbourne. While at UC San Diego, she was awarded a Hellman Fellowship (2006–07). In 2013, she was awarded the Moran Medal from the Australian Academy of Science, for her contribution to "contemporary statistical problems". From 2013 to 2018, she is an ARC Future Fellow, investigating new nonparametric statistical methods. She is a Fellow of the Institute of Mathematical Statistics for her work in "non-parametric function estimation, measurement error problems, and functional data". She is also an elected member of the International Statistical Institute. In 2018 she became a Fellow of the American Statistical Association and in May 2020 she was elected Fellow of the Australian Academy of Science.