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Big Data Analytics and Information Science for Business and Biomedical Applications
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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.
This book is included in DOAB.
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Keywords
- Abdominal Aortic Aneurysm
- ant colony system
- asymptotic theory
- bayesian spatial mixture model
- causal and dilated convolutional neural networks
- deep learning
- DWD
- EEG/MEG data
- elastic net
- Emulation
- ensembling
- entropy-based robust EM
- estimation consistency
- feature fusion
- Feature representation
- financial time series
- generalized linear models
- high dimension
- high dimensional predictors
- high dimensional time-series
- high-dimensional
- High-dimensional data
- Humanities
- information complexity criteria
- inverse problem
- L2-consistency
- LASSO
- Medicare data
- missingness mechanism
- mixture regression
- model selection
- multicategory classification
- nonlocal prior
- nonparamteric boostrap
- nuisance
- penalty methods
- post-selection inference
- prediction
- proximal algorithm
- random subspaces
- regularization
- segmentation
- Social interaction
- Social issues & processes
- Society & culture: general
- Society & Social Sciences
- sparse group lasso
- sparse PCA
- stepwise regression
- strong selection consistency
- Text Mining
- thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues
- thema EDItEUR::N History and Archaeology::NH History
- Trend Analysis
- unconventional likelihood
Links
DOI: 10.3390/books978-3-0365-3192-2Editions