光華講壇——社會(huì)名流與企業(yè)家論壇第6613期
主 題：Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health應(yīng)用于移動(dòng)健康的多分辨率數(shù)據(jù)個(gè)性化動(dòng)態(tài)模型
主講人：加州大學(xué)爾灣分校 Annie Qu教授
主持人：統(tǒng)計(jì)學(xué)院 林華珍教授
時(shí)間：7月16日 15:00-16:00
舉辦地點(diǎn)：柳林校區(qū)弘遠(yuǎn)樓408會(huì)議室
主辦單位：統(tǒng)計(jì)研究中心和統(tǒng)計(jì)學(xué)院 國(guó)際交流與合作處 科研處
主講人簡(jiǎn)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，加州大學(xué)爾灣分校統(tǒng)計(jì)系Chancellor’s Professor。她于1998年獲得賓夕法尼亞州立大學(xué)統(tǒng)計(jì)學(xué)博士學(xué)位。她的研究重點(diǎn)在于解決結(jié)構(gòu)化和非結(jié)構(gòu)化大規(guī)模數(shù)據(jù)的基本問(wèn)題，并開(kāi)發(fā)尖端的統(tǒng)計(jì)方法和理論，應(yīng)用于機(jī)器學(xué)習(xí)和個(gè)性化醫(yī)學(xué)算法、文本挖掘、推薦系統(tǒng)、醫(yī)學(xué)影像數(shù)據(jù)以及復(fù)雜異質(zhì)數(shù)據(jù)的網(wǎng)絡(luò)數(shù)據(jù)分析。新開(kāi)發(fā)的方法可以從大量密集收集的數(shù)據(jù)（例如移動(dòng)健康數(shù)據(jù)）中提取重要且相關(guān)的信息。她的研究影響了多個(gè)領(lǐng)域，包括生物醫(yī)學(xué)研究、基因組研究、公共衛(wèi)生研究、社會(huì)和政治科學(xué)。
在加入加州大學(xué)爾灣分校之前，她是伊利諾伊大學(xué)厄巴納-香檳分校的統(tǒng)計(jì)學(xué)Data Science Founder Professor，并擔(dān)任伊利諾伊大學(xué)厄巴納-香檳分校統(tǒng)計(jì)學(xué)辦公室主任。她被 UIUC 的 LAS 學(xué)院授予 Brad and Karen Smith Professorial Scholar，并在 2004-2009 年獲得 NSF Career award。她是國(guó)際數(shù)理統(tǒng)計(jì)學(xué)會(huì)（IMS）、美國(guó)統(tǒng)計(jì)學(xué)會(huì)（ASA）和美國(guó)科學(xué)促進(jìn)會(huì)（AAAS）的Fellow,她還是IMS Medallion Award and Lecturer 獲得者。她是JASA Theory and Methods的co-editor（2023-2025），并從2021年到2027年擔(dān)任IMS Program Secretary。
內(nèi)容簡(jiǎn)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.
移動(dòng)健康由于智能手機(jī)和可穿戴設(shè)備的普及和強(qiáng)大功能，已經(jīng)成為追蹤個(gè)人健康狀態(tài)的重大成功。這也帶來(lái)了處理異質(zhì)、多分辨率數(shù)據(jù)的巨大挑戰(zhàn)，因?yàn)檫@些數(shù)據(jù)由于個(gè)體不規(guī)則的多變量測(cè)量而普遍存在于移動(dòng)健康中。在本文中，主講人提出一種用于不規(guī)則多分辨率時(shí)間序列數(shù)據(jù)的個(gè)性化動(dòng)態(tài)潛在因子模型，以插值低分辨率時(shí)間序列的未采樣測(cè)量值。該方法的一個(gè)主要優(yōu)勢(shì)是能夠通過(guò)將多分辨率數(shù)據(jù)映射到潛在空間來(lái)整合多個(gè)不規(guī)則時(shí)間序列和多個(gè)個(gè)體。此外，所提出的個(gè)性化動(dòng)態(tài)潛在因子模型適用于通過(guò)個(gè)性化動(dòng)態(tài)潛在因子捕捉異質(zhì)縱向信息。在理論上，主講人提供所提估計(jì)器的集成插值誤差界限，并通過(guò)B樣條近似方法推導(dǎo)出收斂速率。模擬研究和智能手表數(shù)據(jù)的應(yīng)用都表明，所提方法相較于現(xiàn)有方法具有優(yōu)越的性能。
