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 electronic resource (246 p.)
• Soggetto topico: Humanities; Social interaction
• Soggetto non controllato: 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
• 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