2016/10/17 by Xing He, Lei Chu, He, Xing +7
Computer Science · Engineering · Environmental Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Soil Geostatistics and Mapping #Sparse and Compressive Sensing Techniques #stat.ME
paper · pdf · doi:10.48550/arxiv.1610.05076
10 pages, 14 figures, 2 tables, Submit to IEEE Access. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
openalex publication_date 2016/10/17 · arxiv created 2018/01/16 · arxiv updated 2018/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data-driven approaches, when tasked with situation awareness, are suitable for complex grids with massive datasets. It is a challenge, however, to efficiently turn these massive datasets into useful big data analytics. To address such a challenge, this paper, based on random matrix theory (RMT), proposes a datadriven approach. The approach models massive datasets as large random matrices; it is model-free and requiring no knowledge about physical model parameters. In particular, the large data dimension N and the large time span T, from the spatial aspect and the temporal aspect respectively, lead to favorable results. The beautiful thing lies in that these linear eigenvalue statistics (LESs) built from data matrices follow Gaussian distributions for very general conditions, due to the latest breakthroughs in probability on the central limit theorems of those LESs. Numerous case studies, with both simulated data and field data, are given to validate the proposed new algorithms.