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B1442
Title: Big imaging data learning: A parallel solution Authors:  Shan Yu - University of Virginia (United States) [presenting]
Guannan Wang - College of William & Mary (United States)
Lei Gao - George Mason University (United States)
Lily Wang - George Mason University (United States)
Abstract: Nowadays, we are living in the era of ``Big Data.'' A significant portion of big data is big imaging data captured through advanced technologies. Explosive growth in imaging 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 spline regressions in parallel. We develop 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 newly developed method is evaluated through several simulation studies and an analysis of the ADNI data.