প্রচ্ছদ

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...

সম্পূর্ণ বিবরণ

সংরক্ষণ করুন:
বিন্যাস: Online
ভাষা:ইংরেজি
প্রকাশিত: MDPI - Multidisciplinary Digital Publishing Institute 2022
বিষয়গুলি:
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
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
thema EDItEUR::U Computing and Information Technology::UY Computer science
অনলাইন ব্যবহার করুন:ONIX_20221206_9783036555492_76
ট্যাগগুলো: ট্যাগ যুক্ত করুন
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সংক্ষিপ্ত: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.

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