Statistics Seminar: Yingying Fan
VIRTUAL LINK: HTTPS://Z.UMN.EDU/STATSeminarsSpring2021
Speaker: Yingying Fan
Title: Large-scale Network Inference
Abstract: Network data is prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of codes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting of degree-corrected mixed membership model, where the null hypothesis assumes that the pair of nodes share the same profile of community memberships. In the simpler case of no degree heterogeneity, the model reduces to the mixed membership model for which an alternative more robust test is also proposed. Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate. Nevertheless, their analytical expressions are unveiled and the unknown covariance matrices are consistently estimated. Under some mild regularity conditions, we establish the exact limiting distributions of the two forms of SIMPLE test statistics under the null hypothesis and contiguous alternative hypothesis. They are the chi-square distributions and the noncentral chi-square distributions, respectively, with degrees of freedom depending on whether the degrees are corrected or not. We also address the important issue of estimating the unknown number of communities and establish the asymptotic properties of the associated test statistics. The advantages and practical utility of our new procedures in terms of both size and power are demonstrated through several simulation examples and real network applications. This talk is based on joint works with Jianqing Fan, Xiao Han and Jinchi Lv.
Bio: Yingying Fan is Professor and Dean's Associate Professor in Business Administration in Data Sciences and Operations Department of the Marshall School of Business at the University of Southern California, Professor in Departments of Economics at USC, and an Associate Fellow of USC Dornsife Institute for New Economic Thinking (INET). She received her Ph.D. in Operations Research and Financial Engineering from Princeton University in 2007. She was Lecturer in the Department of Statistics at Harvard University from 2007-2008. Her research interests include statistics, data science, machine learning, economics, and big data and business applications. Her latest works have focused on statistical inference for networks and deep learning models empowered by some most recent developments in random matrix theory and statistical learning theory.
Her papers have been published in journals in statistics, economics, computer science, information theory, and biology. She is the recipient of Fellow of Institute of Mathematical Statistics (2020), Fellow of American Statistical Association (2019), NIH R01 Grant (2018), the Royal Statistical Society Guy Medal in Bronze (2017), the American Statistical Association Noether Young Scholar Award (2013), NSF Faculty Early Career Development (CAREER) Award (2012), Zumberge Individual Award from USC's James H. Zumberge Faculty Research and Innovation Fund (2010), USC Marshall Dean's Award for Research Excellence (2010), and NSF Grant (2009), as well as a Plenary Speaker at the 2011 Institute of Mathematical Statistics Workshop on Finance, Probability, and Statistics held at Columbia University. She has served as an associate editor of Journal of the American Statistical Association (2014-present), Journal of Econometrics (2015-2018), Journal of Business & Economic Statistics (2018-present), The Econometrics Journal (2012-present), and Journal of Multivariate Analysis (2013-2016), as well as on the Institute of Mathematical Statistics Committee for the Peter Hall Early Career Prize (2020-2023).
All School of Statistics Seminars are open to the public. A virtual social will take place from 11:30-12:00, following this seminar. To join, visit z.umn.edu/STATSeminarSocial
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