主 题：Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health应用于移动健康的多分辨率数据个性化动态模型

主讲人：加州大学尔湾分校Annie Qu教授

主持人：统计学院林华珍教授

时间：2024年7月16日（周二）上午10：00-11：00

举办地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 国际交流与合作处 科研处

主讲人简介：

Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027.

Annie Qu，加州大学尔湾分校统计系Chancellor’s Professor。她于1998年获得宾夕法尼亚州立大学统计学博士学位。她的研究重点在于解决结构化和非结构化大规模数据的基本问题，并开发尖端的统计方法和理论，应用于机器学习和个性化医学算法、文本挖掘、推荐系统、医学影像数据以及复杂异质数据的网络数据分析。新开发的方法可以从大量密集收集的数据（例如移动健康数据）中提取重要且相关的信息。她的研究影响了多个领域，包括生物医学研究、基因组研究、公共卫生研究、社会和政治科学。

在加入加州大学尔湾分校之前，她是伊利诺伊大学厄巴纳-香槟分校的统计学Data Science Founder Professor，并担任伊利诺伊大学厄巴纳-香槟分校统计学办公室主任。她被 UIUC 的 LAS 学院授予 Brad and Karen Smith Professorial Scholar，并在 2004-2009 年获得 NSF Career award。她是国际数理统计学会（IMS）、美国统计学会（ASA）和美国科学促进会（AAAS）的Fellow,她还是IMS Medallion Award and Lecturer 获得者。她是JASA Theory and Methods的co-editor（2023-2025），并从2021年到2027年担任IMS Program Secretary。

内容简介：

Mobile health has emerged as a major success in tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data which arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. In theory, we provide the integrated interpolation error bound of the proposed estimator and derive the convergence rate with B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.

移动健康由于智能手机和可穿戴设备的普及和强大功能，已经成为追踪个人健康状态的重大成功。这也带来了处理异质、多分辨率数据的巨大挑战，因为这些数据由于个体不规则的多变量测量而普遍存在于移动健康中。在本文中，主讲人提出一种用于不规则多分辨率时间序列数据的个性化动态潜在因子模型，以插值低分辨率时间序列的未采样测量值。该方法的一个主要优势是能够通过将多分辨率数据映射到潜在空间来整合多个不规则时间序列和多个个体。此外，所提出的个性化动态潜在因子模型适用于通过个性化动态潜在因子捕捉异质纵向信息。在理论上，主讲人提供所提估计器的集成插值误差界限，并通过B样条近似方法推导出收敛速率。模拟研究和智能手表数据的应用都表明，所提方法相较于现有方法具有优越的性能。