Big Data Analytics and Information Science for Business and Biomedical Applications II

Big Data Analytics and Information Science for Business and Biomedical Applications II

The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both appl...

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Detalles Bibliográficos
Otros Autores: Ahmed, S. Ejaz (Editor), Nathoo, Farouk (Otro)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Materias:
Information technology industries
Computer science
bandwidth selection
correlation
edge-preserving image denoising
image sequence
jump regression analysis
local smoothing
nonparametric regression
spatio-temporal data
linear mixed model
ridge estimation
pretest and shrinkage estimation
multicollinearity
asymptotic bias and risk
LASSO estimation
high-dimensional data
big data adaptation
dividend estimation
options markets
weighted least squares
online health community
social support
network analysis
cancer
functional principal component analysis
functional predictor
linear mixed-effects model
mobile device
sparse group regularization
wearable device data
Bayesian modeling
functional regression
gestational weight
infant birth weight
joint modeling
longitudinal data
maternal weight gain
transfer learning
deep learning
pretrained neural networks
chest X-ray images
lung diseases
causal structure learning
consistency
FCI algorithm
high dimensionality
nonparametric testing
PC algorithm
fMRI
functional connectivity
brain network
Human Connectome Project
statistics
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009710519806719
Descripción
Sumario:The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased.
Descripción Física:1 electronic resource (196 p.)
ISBN:9783036555508
Acceso:Open access

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