Reprint
Big Data Analytics and Information Science for Business and Biomedical Applications
Edited by
February 2022
246 pages
- ISBN978-3-0365-3193-9 (Hardback)
- ISBN978-3-0365-3192-2 (PDF)
This book is a reprint of the Special Issue Big Data Analytics and Information Science for Business and Biomedical Applications that was published in
Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary
The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
high-dimensional; nonlocal prior; strong selection consistency; estimation consistency; generalized linear models; high dimensional predictors; model selection; stepwise regression; deep learning; financial time series; causal and dilated convolutional neural networks; nuisance; post-selection inference; missingness mechanism; regularization; asymptotic theory; unconventional likelihood; high dimensional time-series; segmentation; mixture regression; sparse PCA; entropy-based robust EM; information complexity criteria; high dimension; multicategory classification; DWD; sparse group lasso; L2-consistency; proximal algorithm; abdominal aortic aneurysm; emulation; deep learning; Medicare data; ensembling; high-dimensional data; Lasso; elastic net; penalty methods; prediction; random subspaces; ant colony system; bayesian spatial mixture model; inverse problem; nonparamteric boostrap; EEG/MEG data; feature representation; feature fusion; trend analysis; text mining