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

• Autore: Ahmed S. Ejaz
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (196 p.)
• Soggetto topico: Computer science; Information technology industries
• Soggetto non controllato: asymptotic bias and risk; bandwidth selection; Bayesian modeling; big data adaptation; brain network; cancer; causal structure learning; chest X-ray images; consistency; correlation; deep learning; dividend estimation; edge-preserving image denoising; FCI algorithm; fMRI; functional connectivity; functional predictor; functional principal component analysis; functional regression; gestational weight; high dimensionality; high-dimensional data; Human Connectome Project; image sequence; infant birth weight; joint modeling; jump regression analysis; LASSO estimation; linear mixed model; linear mixed-effects model; local smoothing; longitudinal data; lung diseases; maternal weight gain; mobile device; multicollinearity; network analysis; nonparametric regression; nonparametric testing; online health community; options markets; PC algorithm; pretest and shrinkage estimation; pretrained neural networks; ridge estimation; social support; sparse group regularization; spatio-temporal data; statistics; transfer learning; wearable device data; weighted least squares
• Persona (resp. second.): NathooFarouk; AhmedS. Ejaz
• Sommario/riassunto: 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.
• Titolo autorizzato: Big Data Analytics and Information Science for Business and Biomedical Applications II
• ISBN: 3-0365-5550-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910637784003321
• Lo trovi qui: Univ. Federico II