Big Data Analytics and Information Science for Business and Biomedical Applications

• Autore: Ahmed S. Ejaz
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (246 p.)
• Soggetto topico: Humanities; Social interaction
• Soggetto non controllato: abdominal aortic aneurysm; ant colony system; asymptotic theory; bayesian spatial mixture model; causal and dilated convolutional neural networks; deep learning; DWD; EEG/MEG data; elastic net; emulation; ensembling; entropy-based robust EM; estimation consistency; feature fusion; feature representation; financial time series; generalized linear models; high dimension; high dimensional predictors; high dimensional time-series; high-dimensional; high-dimensional data; information complexity criteria; inverse problem; L2-consistency; Lasso; Medicare data; missingness mechanism; mixture regression; model selection; multicategory classification; nonlocal prior; nonparamteric boostrap; nuisance; penalty methods; post-selection inference; prediction; proximal algorithm; random subspaces; regularization; segmentation; sparse group lasso; sparse PCA; stepwise regression; strong selection consistency; text mining; trend analysis; unconventional likelihood
• Persona (resp. second.): NathooFarouk; AhmedS. Ejaz
• Sommario/riassunto: 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.
• Titolo autorizzato: Big Data Analytics and Information Science for Business and Biomedical Applications
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557614803321
• Lo trovi qui: Univ. Federico II