Dr. Shan Yu, Department of Statistics, University of Virginia presents Big Spatial Data Learning: A Parallel Solution

Date: Thursday, September 21, 2023

Start time: 3:00 pm

End time: 4:00 pm

Location: Harris Hall room 4119

Audience: All are welcome to attend

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 largescale 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 asymptotic 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

Dr. Shan Yu is an Assistant Professor in the Department of Statistics at the University of Virginia. She received her PhD from Iowa State University in 2020. Her research interests include focuses on statistical methods for complex-structured data, statistical machine learning, and big data analytics.

Sponsor(s): SSOR

Event contact: Dr. Chenlu Ke, kec2@vcu.edu