STATISTICS COLLOQUIUM
Lily Wang
Professor of Statistics
George Mason University
Big spatial data learning: a parallel solution
Abstract
Nowadays, we are living in the era of “Big Data.” A significant portion of big data is big spatial data captured through advanced technologies or large-scale simulations. Explosive growth in spatial and spatiotemporal data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. In general, it uses multiple processing elements simultaneously to solve a problem. However, it is hard to execute the conventional spatial regressions in parallel. This talk will introduce a novel parallel smoothing technique for generalized partially linear spatially varying coefficient models, which can be used under different hardware parallelism levels. Moreover, conflated with concurrent computing, the proposed method can be easily extended to the distributed system. Regarding the theoretical support of estimators from the proposed parallel algorithm, we first establish the asymptotical normality of linear estimators. Secondly, we show that the spline estimators reach the same convergence rate as the global spline estimators. The proposed method is evaluated through extensive simulation studies and an analysis of the US loan application data.
Bio: Lily Wang is a professor of Statistics at George Mason University. She received her PhD in Statistics from Michigan State University in 2007. Prior to joining Mason in 2021, she was on the faculty of Iowa State University (2014-2021) and the University of Georgia (2007-2014).
Wang is highly regarded internationally for her work on non/semi-parametric regression methods. She has broad interests across statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging data, and survey sampling. Working at the interface of statistics, mathematics, and computer science, she is also interested in developing cutting-edge statistical methods for solving issues related to data science and big data analytics. The methods she developed have a wide application in economics, engineering, neuroimaging, epidemiology, environmental studies, and biomedical science.
She is a fellow of both the Institute of Mathematical Statistics (2020) and the American Statistical Association (2021) and an Elected Member of the International Statistical Institute (2008). She is the recipient of multiple NSF awards, SEC Research Fellowship (2019-2020) and ASA/NSF/BLS Senior Research Fellowship (2020-2011), Mid-Career Achievement in Research Award (2021), COVID-19 Exceptional Effort Research Impact Award (2021) from Iowa State University and the M. G. Michael Research Award (2012) from the University of Georgia.
Wednesday, October 13, 2021
4:00 p.m. EDT, 1-hour duration
| Join by meeting number | Meeting number (access code): 2622 735 1106 | Meeting password: maF2iJC8qf8 |
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For more information, contact: Tracy Burke at tracy.burke@uconn.edu