Big Data Analytics and Information Science for Business and Biomedical Applications

Big Data Analytics and Information Science for Business and Biomedical Applications

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...

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Bibliographic Details
Other Authors: Ahmed, S. Ejaz (Editor), Nathoo, Farouk (Editor)
Format: Book Chapter
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
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
Online Access:Get Fullteks
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Description
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.
Physical Description:1 electronic resource (246 p.)
ISBN:books978-3-0365-3192-2
9783036531939
9783036531922
Access:Open Access

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