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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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Gardado en:
Formato: Online
Idioma:inglés
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
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
thema EDItEUR::N History and Archaeology::NH History
thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues
Acceso en liña:ONIX_20220321_9783036531939_28
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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.

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