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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Библиографические подробности
Формат:	Online
Язык:	английский
Опубликовано:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Предметы:	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
Online-ссылка:	ONIX_20220321_9783036531939_28
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Big Data Analytics and Information Science for Business and Biomedical Applications

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