Big Data Analytics and Information Science for Business and Biomedical Applications
Material type:![Article](/opac-tmpl/lib/famfamfam/AR.png)
- books978-3-0365-3192-2
- 9783036531939
- 9783036531922
- Humanities
- Social interaction
- 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
- 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
Item type | Home library | Collection | Call number | Materials specified | Status | Date due | Barcode | |
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OPJGU Sonepat- Campus | E-Books Open Access | Available |
Open Access star Unrestricted online access
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.
Creative Commons https://creativecommons.org/licenses/by/4.0/ cc https://creativecommons.org/licenses/by/4.0/
English
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